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ChatGPT

Chatbot

Intelligent Tutoring Systems

Higher Education

Academic Integrity

Ethical Considerations

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Chatbot

AI assistant

natural language processing (NLP)

machine learning (ML) algorithms

Python

AI-powered language model

AutoML

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Impact of ChatGPT on students

AI

Higher education

Self-guided learning

ChatGPT

Pedagogical enhancement

Online learning

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The article provides a bibliography of academic work that offers clarity, substance, and nuance to enrich the discourse on AI in education. It includes explanations of AI concepts and provides historical context for today’s systems. It describes a range of possible educational applications as well as adverse impacts, such as learning loss and increased inequity. Some articles touch on philosophical questions about AI in relation to learning, thinking, and human communication. Others will help educators prepare students for civic participation around concerns including information integrity, impacts on jobs, and energy consumption.

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Artificial Intelligence and Education: A Reading List - JSTOR Daily

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A bibliography to help educators prepare students and themselves for a future shaped by AI-with all its opportunities and drawbacks.

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The use of artificial intelligence (AI) technologies can lead to unintended but harmful consequences, such as privacy intrusion, discrimination based on gender, race/ethnicity, sexual orientation, or gender identity, and opaque decision-making. The report highlights the recent proliferation of documents charting AI principles and frameworks and how the media covers AI-related ethical issues. It also reviews ethics-related research presented at AI conferences and what kind of ethics courses are offered by computer science departments at universities worldwide.

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The use of artificial intelligence (AI) technologies can lead to unintended but harmful consequences, such as privacy intrusion, discrimination based on gender, race/ethnicity, sexual orientation, or gender identity, and opaque decision-making. The report highlights the recent proliferation of documents charting AI principles and frameworks and how the media covers AI-related ethical issues. It also reviews ethics-related research presented at AI conferences and what kind of ethics courses are offered by computer science departments at universities worldwide.

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The page provides resources and good practice advice for higher education providers and their staff to meet new challenges and benefit from opportunities afforded by advances in generative artificial intelligence (AI). The resources are divided into several categories: academic integrity, assessment, teaching and learning, and international approaches. The page also includes links to webinars produced by TEQSA in association with Deakin University, which address what is known about generative AI, implications for learning, teaching and assessment, and how such technologies might transform higher education into the future.

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These resources aim to assist higher education providers and their staff to meet new challenges and benefit from opportunities afforded by advances in generative artificial intelligence (AI). In addition to resources developed by TEQSA, we have also included good practice advice from other sources.

This area is rapidly changing and evolving. If you have developed or found a useful resource you wish to share, please let us know via our webform.

TEQSA resources

Generative artificial intelligence for higher education webinar series

TEQSA, in association with Deakin University, produced this series of webinars which address what is known at present about generative AI, implications for learning, teaching and assessment and how such technologies might transform higher education into the future.

Academic integrity, assessment and AI

Generative AI in research

Incorporating AI in your classes

Engaging with students about AI

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Understanding generative AI

These resources contain information to assist you with building your understanding about generative AI.
TEQSA makes the information on this webpage available to assist higher education providers, ELICOS providers and foundation program providers in building good practice. It has been obtained from a range of external sources and has not been generated by or on behalf of TEQSA unless otherwise noted. You should read, and carefully consider, the disclaimer before accessing any of the material.

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These resources aim to assist higher education providers and their staff to meet new challenges and benefit from opportunities afforded by advances in generative artificial intelligence (AI). In addition to resources developed by TEQSA, we have also included good practice advice from other sources. This area is rapidly changing and evolving.

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the study shows student experience with chatbot

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the study shows student experience with chatbot

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International Journal of Educational Technology in Higher Education 19, Article number: 57 (2022) Cite this article

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Abstract

Chatbot usage is evolving rapidly in various fields, including higher education. The present study's purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student's question. A pretest-posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data was garnered utilizing an academic achievement test and focus groups, which allowed more in depth analysis of the students' experience with the chatbot. The results of the study demonstrated that the students who interacted with the chatbot performed better academically comparing to those who interacted with the course instructor. Besides, the focus group data garnered from the experimental cohort illustrated that they were confident about the chatbot's integration into the course. The present study essentially focused on the learning of the experimental cohort and their view regarding interaction with the chatbot. This study contributes the emerging artificial intelligence (AI) chatbot literature to improve student academic performance. To our knowledge, this is the first study in Ghana to integrate a chatbot to engage undergraduate students. This study provides critical information on the use and development of virtual teaching assistants using a zero-coding technique, which is the most suitable approach for organizations with limited financial and human resources.

Introduction

There has been a rapid increase in chatbot usage in various fields in recent years. Notably, one essential field where chatbots and virtual assistants are increasingly employed is education (Clarizia et al., 2018). According to Agarwal et al. ( 2022) chatbots are software applications, which are able recognize patterns from inputs and produce outcomes as per the input. The chatbots are called virtual assistants, when they are designed to understand the needs of their users, through artificial intelligence (AI) methods, and reply back to them in natural language. In education, there has been a considerable growth in the representation of chatbots whose major goal is to develop knowledge for individual students, usually on a certain topic. The main goal of these chatbots is to develop new knowledge like a human teacher (Han & Lee, 2022; Pérez et al., 2020). Chatbots are now used as virtual assistants or agents to enhance learning and teaching. The increased use of chatbots is attributable to current advances in Natural Language Processing (Adamopoulou & Moussiades, 2020). More readily available computing power and communication technologies have facilitated the rapid development and deployment of chatbots (Maroengsit et al., 2019) in education. Besides, chatbots can help higher education institutions improve their current services, cut staff expenses, and develop innovative services (Hien et al., 2018). Pérez et al. ( 2020) identify two types of educational chatbots: Service‐oriented chatbots and Teacher‐oriented chatbots. Service‐oriented chatbots offer support for student queries during enrollment and admissions, and library services. Teacher-oriented chatbots act like classroom assistants (Chou et al., 2021) to generate knowledge, increase student engagement and provide smart feedback (Khan, 2019; Vázquez-Cano et al., 2021). Several chatbot development platforms produce education bots that engage students and contribute brief but relevant knowledge (Kumar, 2021). The development of some chatbots may require complex computational skills, while others may require zero coding techniques. Flow XO, DialogFlow, and Botsify are examples of chatbots that anyone, not necessarily tech-savvy, can consider when aiming to implement such solutions in their organizations (Satam et al., 2020). With the advancement of bot platform features, the user interface of the platform becomes very simple and intuitive to enable educational institutions, with limited software development and human resource capacity to create the bots by themselves.

Current research highlights that chatbots may address the challenge of insufficient student-instructor interaction (Clarizia et al., 2018). Especially in contexts like the Ghanaian higher education institutions, where the instructor-student ratio is high (Essel et al., 2019; Tsyhaniuk & Akenten, 2021), the use of a chatbot may provide automatic and instantaneous responses to students' queries similar to chatting with a course instructor. This leads to reduced workload for the instructor and more engaging learning experience for the students. In this study we implemented a zero-coding chatbot, named KNUSTbot, in a multimedia programming course at Kwame Nkrumah University of Science and Technology (KNUST), which allows students to learn and reflect profoundly about multimedia programming via interaction. The effectiveness of student interaction with the KNUSTbot during the learning process was examined. The chatbot in the present study is a teaching assistant chatbot developed to accomplish a set of learning objects by determining intents and entities from a free text communication of a student leveraging Natural Language Processing without confining the student with a set of available options. This method allows for a more natural way of engaging (Yin et al., 2021).

The study's Research Question (RQ) is the following:

How does the KNUSTbot, used as an intermediary between students and instructors, affect the student's learning in a multimedia programming undergraduate course?

The following are the study's sub-research questions:

1. 1.
Is there a significant difference in academic results between students who interact with a chatbot and students interacting with a course instructor?

2. 2.
What are the perceptions of the experimental cohort on the chatbot as a means to facilitate their learning?

Chatbots in teaching and learning

Chatbots combine artificial intelligence (AI) and Natural Language Processing to interact with a human interlocutor at a certain level of conversation via text or voice (Pérez et al., 2020; Smutny & Schreiberova, 2020). Clarizia et al. ( 2018) describe chatbots as virtual assistants capable of answering questions and providing appropriate responses. Other authors adopted a text-based chatbot, which typically responds to questions by following a built-in rule set, allowing them to respond to their users (Budiu, 2018; Salas-Pico & Yang, 2022; Topal et al., 2021). AI relates to systems or machines that mimic human intelligence and self-alter based on accumulated data (Angelov et al., 2021). Chatbots are an example of software applications (Salas-Pico & Yang, 2022; Topal et al., 2021) that understand questions faster and provide efficient answers (Angelov et al., 2021). Examples of chatbots include FAQ chatbot (Han & Lee, 2022; Ranoliya et al., 2017), ELIZA, an early Natural Language Processing computer program that simulated the communication between humans and machines (Natale, 2019), the colMOOC, an conversational virtual agent that promotes learners' interaction within MOOC platforms (Tegos et al., 2019), the StudBot, which is an academic information systems chatbot (Vijayakumar et al., 2019) and the artificially intelligent course teacher chatbots, like Sammy (Gupta et al., 2019), which is closer to what we are experimenting in our study. These chatbots have received significant recognition in the educational ecosystem in diverse learning contexts.

Okonkwo and Ade-Ibijola ( 2020) reported that most chatbots employed in higher education are teacher-oriented chatbots. Mendoza et al. ( 2020) highlighted positive impressions from a sample of students when they were engaged with a chatbot. Studies have also reported that students employ chatbots to ask questions, receive responses, and receive individualized support (Hiremath et al., 2018; Mikic-Fonte et al., 2018; Pham et al., 2018; Sinha et al., 2020).

According to Yin et al. ( 2021), no significant difference in the learning achievements of undergraduate students randomized into experimental and control groups (without or with the support of a chatbot) were found; however, the research reported higher levels of motivation for the learners interacting with the chatbot. Arruda et al. ( 2019) designed a chatbot for computer science students, employed for goal-oriented requirements modelling; the students found the chatbot functional and desired to use it in the future. A study performed by Kamita et al. ( 2019) using chatbots and web courses to improve students' mental health reported a higher probability of efficacy as chatbots guided self-learning, enhanced motivation, and lessened stress. The University of Georgia designed a chatbot named 'Jill Watson' adopted in a computer science course. Participating students were more responsive and they stated that they wanted to use this chatbot in different lessons (Lipko, 2016).

Students' ability to interact with instructors, by asking questions, is an essential process of learning that can contribute to enhanced academic performance (Harper et al., 2003; Sandu & Guide, 2019; Vlachopoulos & Makri, 2021). University students in Ghana have inadequate interaction with their course instructors during class sessions. This issue is due to the increase in the student-instructor ratio (Essel et al., 2019), reducing time instructors spend with their students. Furthermore, studies have shown that students are hesitant to ask questions because they are constantly scared of the teacher's negative feedback (Oktaria & Soemantri, 2021; Verleger & Pembridge, 2018). In mitigating these issues, some instructors engage students after classroom didactics, with instant messengers (e.g. WhatsApp) and social media platforms (e.g. Facebook messenger) to provide personalized assistance to the students. However, the challenge is the instructor not having enough time to respond to questions and provide timely and individualized feedback to students. The unspontaneous student-instructor interaction leads to shortcomings in student's knowledge. Late response to a student's question is a significant concern as students continuously strive for precise and prompt responses (Farhan et al., 2012). In this context, chatbots become relevant in situations where course instructors cannot provide adequate response for students' learning at any time of the day (Yang & Evans, 2019). A chatbot can simulate human-like dialogue-based interactive communications to assist students in revisiting learning resources (Göschlberger & Brandstetter, 2019; Jomah et al., 2016; Smith & Evans, 2018), promoting learning achievement and self-efficacy (Chang et al., 2021) and enhancing adaptive learning (Fadhil & Villafiorita, 2017).

Furthermore, chatbots can assist in overcoming this difficulty by initiating conversations based on the student's context, making students seem individually addressed (Hien et al., 2018; Howlett, 2017). A chatbot can be an intermediary between a student and an instructor, which allows students to concurrently control their learning and improvement at their pace without constraining them (Wang et al., 2021). Also, chatbots tend to stimulate questions from students who may be restrained from engaging in a conventional learning space (Verleger & Pembridge, 2018).

Method

We employed a quasi-experimental pretest-posttest design in combination with a qualitative research method (focus groups), to collected more in-depth information on the students' experience with the chatbot. The 68 participating undergraduate students were in their final year in a multimedia programming course offered by the Department of Publishing Studies at Kwame University of Science and Technology (KUNST). The experiment was conducted in the second semester of the 2021 academic year between April and August (16 weeks). Stratified sampling was chosen to create two groups with representative sample from the student population (Delice, 2010), which was divided into relatively similar subpopulations (strata) in terms of age, gender and academic performance. As part of the study, we garnered data on their socio demographics, including gender, age, cumulative weighted average grade, preferred instant messaging app, and previous experiences with AI chatbots.

The intervention procedure

We used the end-of-semester course achievement tests to measure the academic performance of the experimental and control groups. The achievement test had two sections. Section 1 contained different types of objective questions (multiple choice with single and double selection, matching, ranked order, and short answer). Section 2 of the test was a computer-based practical examination where students scripted a front-end website using HTML (Hypertext Markup Language) and CSS (Cascading Style Sheet). The lead investigator developed the test items. There were 40 objective-typed items in section A, and each item carried 1 point, and section B carried 60 points. The total maximum score for the test is 100 points. The reliability coefficient of the items was = 0.82, with the average difficulty level estimated at p = 0.45. According to Hasançebi et al. ( 2020), values ranging between 0.30 and 0.49 suggest average item difficulty. The number of correct responses provided by students was used to determine their achievement. The total time estimate for the achievement test was 60 min (20 min for section A and 40 min for section B). The investigators developed the table of specifications based on the university's standards to measure the content and construct validity of the test.

In addition, the investigators designed group interview (focus group) guide to collect comprehensive data about the students' experience with the chatbot. The guide was given to the experimental group to estimate their observations of the chatbot. The focus group discourse was done in a single sitting, with physical presence and one week after the experiment and every student in the experimental group partook. To give more opportunities for interaction to all students, three focus groups were conducted. The focus groups aimed at interpreting the students' positive and negative encounters with the chatbot (KNUSTbot) for course interaction and their predisposition to interact with the KNUSTbot in future academic endeavors.

The KNUSTbot was developed using WhatsApp instant messaging app and Flow program. FlowXO is a Natural Language Processing platform that can build human-like conversational AI with state-of-the-art virtual agents in multiple languages and platforms (Flow, 2020). Two experienced multimedia programming experts and an instructional designer verified the KNUSTbot. The chatbot's intents and entities comprise a data repository of standard learning content queries (HTML and CSS) built based on chats database continuously accumulated in a Learning Management System over eight years by the lead investigator. Current studies (Snodgrass Rangel et al., 2017) highlight that student data obtained by teaching staff during teaching and learning process has the potential to improve education, as well as the way educational institutions work. We understand that some types of research using quantitative, qualitative or mixed methods, may require prior contact and dialogue with individuals or communities as a normal and integral component to establish the design of the research and understand its value for the society and the discipline in question. These activities don't require ethics review and approval (Government of Canada.-Panel on Research Ethics, 2018). We considered that the accumulation of student queries prior to the beginning of the "approved" study fall in this category, since the instructor didn't invite the students for this interaction and simply used the published queries within the Learning Management System (LMS) of the university. In addition, these queries are not linked to any personal information, such as name, email, group, year of studies, etc. Finally, all students gave their consent to participate in the LMS, where this information is published.

The term "intent" describes the question a student requested, to which the chatbot is supposed to answer. At the same time, the entity represents a trigger connected to the intent to render a distinct and individualized context for the intent. In all, 1000 intents and entities were used as a dataset to train the bot. We included additional 70 intents (short text expressions, e.g. 'Hi super programmer', 'Good job', 'I'm always at your service') to engage and motivate the students during the interaction. Besides, the bot was trained with internet sources (e.g. Websites and YouTube videos).

Whatsapp was selected because of its user-friendliness and popularity among undergraduate students (Afful & Akrong, 2019). The students interacted with the chatbot using natural language. The interface of the KNUSTbot and how a student can interact with it is illustrated in Fig. 1.

A digital literacy test was administered to the students to ascertain their prior abilities on basic computer skills, the internet and the web, productivity programs, computer security and privacy, and digital lifestyle. This activity occurred in week one of the course and constituted the pre-test score. All 68 students were exposed to multimedia programming with HTML and CSS through face-to-face instruction from the second week to the sixteenth week. The course instructor demonstrated how to develop a front-end application using the scripting languages in a practical session. During the sessions, the instructor created two WhatsApp platforms for the control and the experimental cohort. The students were instructed to direct content-related questions to the WhatsApp platform. The experimental cohort was assigned to the WhatsApp group via the platform integrated with the chatbot. They engaged the chatbot at any time after a topic was taught; however, the instructor did not intervene in the experimental cohort's interactions. No additional communication channels or contact points were available for the two groups. The control cohort interacted on the platform without the chatbot. The two cohorts were assessed with a post-learning achievement test at the end of the sixteenth week.

After the experiment with the use of the chatbot, focus groups were conducted by the lead investigator, who is an experienced scholar in educational research. Students in each group were initially asked similar questions designed to explore their perceptions of the educational experience they had with the chatbot and to probe for the positive and adverse viewpoints they highlighted from their experience. Another question honed in on whether students felt they have derived similar benefits from their interaction with the chatbot compared to the interaction they were used to have with the instructor. The last question of the focus group was related to whether students consider the benefits of interaction with the chatbot significant, so it can be implemented in other courses of their program of studies. Each focus group lasted approximately 45 min. Figure 2 depicts the intervention procedure.

Data analysis and ethical considerations

The Jamovi 2.0.0 package (Jamovi project, 2021) was used to analyze the data using descriptive and predictive statistics. The Shapiro-Wilk test (p > 0.05) indicated that the datasets were normally distributed. As a result, we obtained skewness and kurtosis values of ± 1.96 for the datasets, validating the normality of distribution (Essel et al., 2021). In the present study, the significance level for statistical tests was estimated at a p-value of less than p < 0.05. Frequency, percentage, Mean, and standard deviation were calculated for the students' sociodemographics. A Split-Plot Analysis of Variance (SPANOVA) was performed to assess the influence of the chatbot and instructor on students' Achievement Test scores over two-time sessions (pre-test and post-test). SPANOVA and independent-samples T-test were used to examine RQ1. All assumptions, including Homogeneity of variance (Levene's: p pretest = 0.08; P posttest =  0.56), and normality of distribution (Shapiro-Wilk: p pretest = 0.06; P posttest =  0.27) were met.

To investigate RQ2, the investigators used the data from the focus groups, which took place at the University premises. The students were interviewed in groups of 11 or 12 people to create the dynamic of a conversation and to make the student feel more at ease (Witsenboer et al., 2022). The data were digitally recorded, transcribed, and manually coded under themes using content analysis. To create a theme, the content was coded in two levels. First level of coding included labels assigned to specifics fragments of the focus group, which could help us answer the RQ2. Following Witsenboer et al. ( 2022) it was also checked whether existing labels could be assigned to overlapping content. In the second level of coding we narrowed the focus to relatively fewer codes, directly related to RQ2.

The present study was conducted under the Helsinki Declaration (1975) and comparable ethical standards, with approval by the Humanities and Social Sciences Research Ethics Committee (HuSSRECC) of KUNST with number 233/22-08/2021. A written and verbal informed consent was solicited from the students.

Results

Participants' profile and traits

This study included 68 final year undergraduate students, 34 of whom were in the experimental group and the other 34 in the control group. All the students had the WhatsApp app installed on their phones. Regarding students' experience with chatbots, all 68 reported that they have encountered chatbots in their online activities outside their studies, with the 79% (54 students) encountering services-oriented chatbots. Table 1 illustrates the profile of the students and some of key traits for the interpretation of the findings. The median split categorized overall academic performance measured with Cumulative Weighted Average (CWA, Median = 65.71) and the years of experience (Median = 5.0) with the use of WhatsApp app.

Engagement with the chatbot

Figure 3 illustrates the preferred time during the day students engage with the chatbot, as well as the amount of interactions (number of chats) per hour. It is obvious that the students interact with the KNUSTbot mostly between 20.00 and 22.00, which is when the instructors are not usually available and responsive.

Figure 4 illustrates the amount of students, who interacted with the chatbot during the course. It is obvious that there were more students engaging at the beginning of the course, when they needed more information to understand how the course works and towards the end of the course, when they were preparing for their final assessment.

Figure 5 illustrates the number of student queries using the chatbot during the course. Again, it is obvious that there was more need for interaction during the last month of the course, when students were preparing for their final assessment.

Academic achievement scores

As shown in Table 2, students in the experimental cohort received an average score of 40.6 (4.95) for course achievement before and 81.1 (3.19) after the experiment. Similarly, students in the control group received mean scores of 43.4 (4.09) and 65.2 (3.73) respectively. The results demonstrate that course achievement improved between the experimental group of students, who interacted on a real-time basis with the chatbot, and students who interacted with the instructor in real-time. Interaction between time and method of interaction, F(1, 66) = 87.5, p < 0.05, squared partial eta (η p ²) = 0.57 was found significant.

Using Cohen's guidelines using effect size: 0.01 = small, 0.06 = medium, 0.14 = big, these results suggested a very large effect size for interaction. In terms of real-time interactions with course instructors ( p < 0.001) and real-time chatbot interactions, there were significant differences between the posttest and pretest scores (p < 0.001) and no significant difference in cohorts before intervention in two groups (p > 0.05), the difference between cohorts following intervention was significant (p < 0.001). The results are illustrated in Tables 3 and 4.

Sociodemographic variables for experimental cohort regarding course achievement

The investigators also wanted to observe after the experiment whether there was a significant difference in course achievement regarding sociodemographics. The results in Tables 5 and 6 illustrate that there was no statistically significant variation in gender ( p > 0.05) and course achievement, as well as age ( p  > 0.05) and course achievement.

In addition, the results show no statistically significant difference between prior academic performance and the posttest score ( p = 0.51), as well as WhatsApp user experience and the posttest score ( p  = 0.34).

Findings from the focus group

Focus group discussions (after posttest) were conducted with the experimental cohort to garner data on the perceptions regarding their interaction with the KNUSTbot. Students were asked to appraise the positive and adverse viewpoints of their interaction with the KNUSTbot during this discussion. Table 7 comprises the thematic codes based on the results from the discussions. Student appraisals of the discourse were utilized to generate the thematic codes.

Most students were satisfied with their interactions with the KNUSTbot during the course, as exposed in Table 7. Below there are some representative examples of student statements confirming the positive viewpoints.

The AI chatbot is easy to use

"I never imagined that a chatbot could be so easy to use through my mobile phone"

"It was very easy to share the responses from the chatbot with other students through WhatsApp. This is the app we use to communicate as well, so it was useful"

"It allowed me to study the course with ease"

Searching for and evaluating information

"I also got links to websites that provided access to video and text tutorials in relation to the course content"

"I didn't only receive an answer to my question, but also sources to consult to better understand the chatbot's answer"

More self-belief to learn more effectively

"I realized that I can find the answers on my own"

"At the beginning I was insecure with the information I received by soon I was able to confirm the answer on my own"

Giving immediate feedback

"I realized that the response to my questions was very swift"

"I was able to get 24/7 quick answers to my questions when learning HTML and CSS which I have never encountered in any other course"

Overall, the students were satisfied with the instantaneous and immediate responses they received to inquiries during chats. They were generally welcoming the usage of the chatbot as a learning tool. They didn't encounter delayed responses to their questions asked within the chat platform compared to their experiences with the instructor, where their questions may have received a delayed response or no answer at all. Despite this positive feedback, student highlighted some negative viewpoints on the integration of the chatbot in their learning, which can be summarized in the statements below:

Concerns about responses being outdated/not relevant

"My experience with the chatbot was not pleasant. I realized I was getting similar answers to the variety of questions I asked during the chat sessions"

"Sometimes it was frustrating asking for one thing and getting an answer about a different topic"

"The majority of the information I had seemed outdated to me. Besides, links/URL provided were broken, and this situation worried me at times"

Inability to think in depth

"I missed more in-depth answers from the chatbot and not simply definitions and links to find additional resources. I feel only the instructor can do this"

"Often I needed more explanation or even justification of the answer I received from the chatbot. I wasn't able to understand what to do with the given information"

"I wished the course instructor supported the AI chatbot at a point in time"

There is a dearth of detailed interactions

"Few times I just received a link or a short answer to my question"

"I wish there were more follow-up answers since not always I understood the information I got from the chatbot"

"Some of my questions couldn't be answered because they were about how to apply the knowledge from the course. I needed more instructions"

After synthesizing the negative feedback received, we can say that the students complained about their interaction with the chatbot because it didn't carry out in-depth learning and the human element was missing. They also acknowledged that the chatbot gave the same responses to different questions. The need for more instructions on how to apply the knowledge acquired from the course was also highlighted.

Notwithstanding, students formed good views after interacting with the chatbot and fully appreciated the interaction approach. The vast majority recommended the integration of chatbots in other courses of their studies and more than half of the participants preferred the chatbot comparing to the interaction with the instructor. Some representative responses are presented below:

"I felt really elated interacting with the chatbot in this course. I had responses to all the questions I asked. At one point, I felt like I was chatting with the instructor. It is simple interacting with the chatbot"

"Interacting with the chatbot is a great way to learn more about HTML and CSS. I expect that chatbot learning will be used in more tailored educational systems in the future"

"The integration of the chatbot into the teaching and learning of multimedia was engaging, motivating and exciting for me, as it was a new experience and I felt more sense of belonging in the course"

"I was surprised by the kind of feedback I received during my conversation with the chatbot. It supplied me with a lot of reference links"

After analyzing the results from the focus groups, it is reassuring that, given the right conditions, students might appreciate the integration of chatbots as part of a course since it simulates and assists them in learning abstract concepts in-depth. This positive response from the students suggests the possibility of increasing the limited interaction sessions between academic staff and students, ensuring self-regulated learning and experiencing novel learning cultures, where academic staff is assisted by integrated emerging technologies into their courses. As a whole, engaging with the chatbot can support students in connecting what they are learning with real-world challenges or precedents, encouraging them to reason in-depth regarding what they are studying.

Discussion

The study indicated that students in the experimental cohort who engaged with the chatbot performed better than students in the control cohort who interacted with the course instructor. The use of chatbots can be a significant progression and innovation for heightening challenging subject learning (Clarizia et al., 2018; Okonkwo & Ade-Ibijola, 2020) such as multimedia programming.

The findings indicate no significant difference related to gender, age, experience with Whatsapp, academic performance, and the post-test scores of the experimental cohort. This confirms Sandu and Gide ( 2019), who also reported no significant difference between gender and age and the adoption of a chatbot. Regarding WhatsApp, our findings demonstrate that years of experience in using it didn't affect the students' post-test score. This is probably because it is a very popular and intuitive app widely used in Ghanaian higher education (Boateng & Tindi, 2022). There was a significant difference between daily Whatsapp use and post-test scores of the experimental cohort. The possible reason for this finding can be attributed to the immediacy of the feedback provided by the chatbot reflecting the improvement of learning, while the instructor usually delayed more to answer due to the timing of the questions were sent (since 2 most questions were sent outside office hours) and the big student-instructor ratio (Essel et al., 2019), which doesn't allow instructor to spend enough time with their students and provide them with timely responses.

The quantitative analysis demonstrates that engaging students with a teaching assistant chatbots positively impacts academic performance. The qualitative analysis provided evidence of students' satisfaction with the use of the chatbot, which can be attributed to the instantaneous feedback they received from the chatbot, as well as the enormous contribution to the learning process via having more engagement with chatbot at different times, and without encountering any delays in the interaction process. One of the purposes of using AI-powered teaching assistant chatbots, according to Chang et al. ( 2021) and Sandu and Gide ( 2019), is to deliver timely knowledge to specific students to surmount difficulties that arise during the learning process. Besides, the comments from the experimental cohort suggest that the student gained understanding and confidence to complete the course which translated in their improved academic performance. They also found learning to be interesting and interactive as their engagement with the chatbot enhanced the organization and re-examination of knowledge acquired. This outcome is consistent with Chang et al. ( 2021) finding that students' awareness arose due to the possibility to grasp and perform in-depth thinking by studying pertinent information.

Conclusions, limitations and implications of the study

The present study's main findings and arguments support the value of using chatbots in higher education, since the students who interacted with the chatbot performed better than students in the control cohort who interacted with the course instructor. This is especially relevant for countries like Ghana, where student-teacher ratio is high and the provision of timely response and feedback to students is a challenge. At the same time, students were very satisfied with the use of the chatbot, mainly because it provided them with instantaneous feedback at different times, without encountering any delays in the interaction process.

However, specific difficulties associated with the AI-powered teaching assistant must be overcome to use this approach effectively. Academic staff must have access and knowledge to customize and integrate chatbots to assist students learning. Since students may have encountered other chatbots such as the service-oriented chatbots, it is also advisable to make the transition to the use of teaching assistant chatbot simpler. Furthermore, it is encouraged that different motivators should be used to urge students to engage the chatbot. For example, the integration of gamification to enhance students' engagement and interaction with chatbot can be a reinforcer, in line with the recommendations of Fadhil and Villafiorita ( 2017). Moreover, a micro-learning approach is a viable strategy for integrating teaching assistant chatbots in the educational setting (Yin et al., 2021). Other instant messaging platforms (Telegram or WeChat) can be employed to encourage students' interactions with teaching assistant chatbots due to their familiarity with these platforms (Boateng & Tindi, 2022).

Though this study engaged students with a chatbot developed with zero coding and in one course, the results are encouraging for the use of a teaching assistant chatbot in similar contexts. Specifically, within an institution/country with very limited resources (human and technological), the fact that we were able to execute such innovation successfully, with positive impact on academic performance and student satisfaction, make us confident that it can enact positive change in the teaching and learning process.

In this context, it is important to mention that the present study has also some limitations. It consists entirely of fourth-year students in a single university department, so future studies may observe students' experience in other years as well as other faculties. This would allow generalize the results on the integration of chatbots in higher education. For example, it is unclear whether students in this study engaged more with the chatbot because they come from a more "technical" discipline and they are already familiar with the use of technology. The outcome may have been different in a humanities course. Also, it is not clear how first year students, who are not yet familiar with the teaching and learning process of the university and the available communication channels, would react to the chatbot service, taking into consideration that first year students are more instructor-dependent (Hagenauer & Volet, 2014). Furthermore, it is important to mention that every instructor reacts differently when it comes to provide timely feedback and creating strong group dynamics with their students, even if they share the same workload with their peers. So the attitude of the instructor can also impact the use of the chatbot. Moreover, while the chatbot can be very useful for facts to be learned, which require clear right answers, it is not clear how the chatbot could support learning that is centered around students' developing ideas about a topic or a body of theory. For all the above-mentioned reasons, we are not aiming to make general claims about the use of the chatbot in higher education, but to explore the efficiency of this cost-effective additional student support service and to expand the research in contexts with high student-instructor ratio.

The results of our research confirmed the existing literature that the use of chatbots enhances self-efficacy and learner achievement. Universities should establish Educational Technology Centers managed by subject matter experts to assist instructors in integrating and engaging students with teaching assistant chatbots. To obtain reliable results, instructors with low levels of digital literacy should receive proper training and coaching. Moreover, hands-on workshops should be organized to urge instructors to embrace the teaching assistant chatbot interaction in supporting learning and teaching. Besides, any course instructor who engages a virtual teaching assistant chatbot with students must ensure that each student is conversant with the instant messaging (WhatsApp, Telegram, or WeChat). The instructor must also ensure that the students have consistent Internet access and examine whether specific students cannot use the instant messaging application. Students may have difficulty adapting to the teaching assistant chatbot when using it for the first time. An initial session should be organized in which the engagement procedure is detailed, and students are informed about its assets to alleviate the affirmed challenges.

Certain directions for additional investigations are made based on the findings and discussion of the present study. It remains unclear whether chatbot can support learning by responding to technical questions mainly (e.g. explaining what is HTML5), or it can help learners to understand the conceptual content better. Future research could look into the impact of chatbots in other areas of knowledge. Also, the academic performance of students who interacted with the chatbot the most and those who interacted with it less could be compared. In addition, the chatbot's longitudinal influence on student engagement and motivation should be investigated. Another identified area of future research is the influence of chatbot use on postgraduate supervision activities. Lastly, future studies could investigate the control cohort's perceptions and test the acceptance of the experimental cohort using the Unified Theory of Acceptance and Use of Technology models, focusing on the intentions of the students when they use the chatbot, as well as on the subsequent usage behavior.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

1. Kwame Nkrumah University of Science and Technology, 315-7530, Kumasi, Ghana

Harry Barton Essel, Akosua Tachie-Menson, Esi Eduafua Johnson & Papa Kwame Baah

2. Rotterdam School of Management, Erasmus University, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, The Netherlands

Dimitrios Vlachopoulos

Contributions

HBE, DV and ATM conceptualized the research. HBE led the ethics approval process. HBE, ATM and PKB constructed the study design and materials used during study collection. EEJ and PKB realized data collection. DV and HBE supervised the data analysis. HBE, ATM, EEJ and PKB wrote the original manuscript draft. DV revised the draft version and created the final version submitted to the journal. All authors contributed in revising the manuscript. All authors read and approved the final manuscript.

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Correspondence to Dimitrios Vlachopoulos.

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Essel, H.B., Vlachopoulos, D., Tachie-Menson, A. et al. The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education. Int J Educ Technol High Educ 19, 57 (2022). https://doi.org/10.1186/s41239-022-00362-6
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Keywords

Zero-coding chatbot

Virtual teaching assistants

Student-instructor interaction

Ghanaian higher education

Artificial intelligence

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The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education - International Journal of Educational Technology in Higher Education

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Chatbot usage is evolving rapidly in various fields, including higher education. The present study's purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student's question. A pretest-posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts).

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What is a virtual assistant (AI assistant)?

A virtual assistant, also called an AI assistant or digital assistant, is an application program that understands natural language voice commands and completes tasks for the user.

Such tasks -- historically performed by a human personal assistant or secretary -- include taking dictation, reading text or email messages aloud, looking up phone numbers, scheduling, placing phone calls and reminding the user about appointments. Popular virtual assistants include Amazon Alexa, Apple Siri, Google Assistant and Microsoft Cortana.

Types of virtual assistants

Though this definition focuses on the digital form of virtual assistants, the terms virtual assistant and virtual personal assistant are also commonly used to describe contract workers who work from home doing administrative tasks typically performed by executive assistants or secretaries.

With the evolution of chatbots and generative AI, various types of virtual assistants have gained popularity. However, they serve different purposes for customers and provide different levels of assistance.

Common types of virtual AI assistants include the following:

AI chatbots. AI chatbots are computer programs that serve as virtual assistants and communicate with users through text-based interfaces on websites, social media platforms and messaging apps. These chatbots can assist customers, respond to inquiries or start a discussion with them. AI chatbots employ natural language processing (NLP) and machine learning (ML) algorithms to understand user input, produce pertinent responses and improve their performance over time by learning from these interactions.

Conversational agents. Beyond text-based chatbots, conversational agents enable virtual assistants to converse in a human-like manner through both text and voice interfaces. These AI-based systems are designed to comprehend intricate linguistic patterns, discern user intent and offer responses that are appropriate in a given situation. Conversational AI can be used to build more sophisticated virtual assistants that can handle various tasks and offer a more intuitive and natural user interface.

AI virtual assistants. These advanced virtual assistants can perform a variety of tasks, often across multiple devices and platforms. They frequently converse with users by using natural language through voice-based interactions. With these AI virtual assistants, users can easily and quickly access information, control smart home appliances and manage their calendars by integrating with a variety of services and applications. Amazon Alexa and Apple Siri are examples of AI virtual assistants.

Virtual assistants are often compared to chatbots and conversational agents, but the three technologies aren't the same.

Virtual assistant devices and technology

Virtual assistants are typically cloud-based programs that require internet-connected devices and applications to work. Most AI virtual assistants can be found on devices such as smartphones, smart speakers or other platforms, including instant messaging apps.

AI chatbots that use generative AI, such as ChatGPT, are also gaining popularity for their ability to generate human-like responses to text-based conversations.

Common virtual assistant technologies and devices include the following:

Siri. Apple's built-in, voice-controlled personal virtual assistant is available on devices using iOS, iPadOS, watchOS, macOS and tvOS. It uses voice recognition technology that's powered by AI.

Cortana. Microsoft's personal productivity assistant Cortana uses the Bing search engine to perform various tasks, including setting reminders and answering user queries.

Copilot. Copilot is an AI assistant built into the Windows 11 operating system that can be used across all Windows apps and programs. It provides a natural user interface through AI-powered chat for interacting with the Windows operating system.

Google Assistant. This virtual voice assistant software application was developed by Google for Android devices. Google Assistant can perform a variety of tasks including answering questions, adjusting hardware settings on the user's device, scheduling events and alarms, and playing games.

Amazon Alexa. Used mainly through Amazon's product line of hands-free speakers known as Amazon Echo, Alexa is a cloud-based voice service available on more than 100 million Amazon devices as well as third-party manufacturers. To use Alexa, users call out the wake word, Alexa and a light on the device signals to the user that it's ready to receive a command, which typically involves simple language requests, such as "what is the weather today?" or "play pop music." Alexa will also gain access to generative AI with its own large language model and use it to enable access to more than 200 smart home APIs. Instead of saying "Alexa, turn on the air conditioning," users can say, "Alexa, I'm hot," and the assistant will turn on the heat using advanced contextual understanding that AI enables.

Samsung's Bixby. Samsung's virtual AI assistant runs primarily on mobile devices but also some smart refrigerators. Bixby can be used for various tasks including texting, retrieving location-specific weather information, setting meeting reminders and reading news articles.

Mycroft AI platform. Mycroft is the first open source voice assistant that can be run on any platform, including desktops, automobiles and even on a Raspberry Pi. It focuses on voice-enabling any device to turn it into a smart personal assistant.

The technologies that power virtual assistants require massive amounts of data, which feed AI platforms, including machine learning, NLP and speech recognition platforms. As the end user interacts with a virtual assistant, the AI programming uses sophisticated algorithms to learn from data input and become better at predicting the end user's needs.

Capabilities of virtual assistants

Virtual assistants typically perform simple jobs for end users, including the following:

Adding tasks to a calendar.

Providing information that would normally be searched in a web browser.

Controlling and checking the status of smart home devices, including lights, cameras and thermostats.

Making and receiving phone calls.

Scheduling meetings.

Creating text messages.

Getting directions.

Hearing news and weather reports.

Finding hotels or restaurants.

Checking flight reservations.

Streaming podcasts.

Setting reminders.

Workflow management.

Assisting in e-learning and training.

Calling an Uber of Lyft.

Listening to music.

Playing games.

Privacy concerns

Some consumers have expressed privacy concerns about virtual assistants, such as Amazon Alexa and Google Home. These virtual assistants require large amounts of personal data and are always listening in order to respond to voice commands, also known as wake words or phrases. Although voice assistants are only intended to begin recording when given the wake command by a user, they may accidentally capture voice exchanges and private information without the user's knowledge.

Cortana, for example, works best by using data from a user's device, including emails and other communications, a user's contacts, location data, search history and data from other Microsoft services and skills -- third-party applications -- that users choose to connect with. Users can choose not to sign in and share this data with Cortana and can adjust permissions to prevent certain data from being collected, though these actions limit the virtual assistant's usefulness.

Virtual assistant providers also maintain privacy policies, which define how each company uses and shares personal information. In most cases, companies don't share customer-identifiable information without a customer's consent but there have been concerns regarding how voice assistant providers use and handle user data.

Besides privacy policies practiced by providers, end users can also take the following security precautions:

The settings of voice assistants can be changed to enable sound notifications, which will alert the user when the assistant is accidentally triggered and begins to record voice interactions. By default, the notifications of certain voice assistants such as Echo and a few Google devices are set to silent.

Users can opt out of recordings of commands and conversations as well as historical records saved on the devices, such as with Amazon Alexa. However, this step doesn't guarantee that the data won't be evaluated once the transcripts are deleted.

Users should practice extra caution when sharing certain types of information with voice assistants. It's best to avoid giving voice assistants access to sensitive information.

Separating business and personal voice assistant profiles can also improve privacy. The majority of assistants -- including Alexa and Google Assistant -- support multiple accounts, enabling users to segregate their personal voice assistants from their professional ones.

The future of virtual assistants

Virtual assistants are quickly evolving to provide more capabilities to users. As speech recognition and NLP have advanced, so too has the virtual assistant's ability to understand and perform requests. And as voice recognition technology continues to improve, virtual assistant use will move deeper into business workflows.

Experts predict that AI assistants will continue to become humanistic and able to provide more personalized experiences as AI technology advances. According to Gartner, by 2025, these AI assistants will become more ubiquitous with about 50% of knowledge workers using a virtual assistant on a daily basis. However, the widespread use of AI assistants doesn't obscure the growing worries about the privacy and security risks associated with them. Companies are being urged to address these concerns transparently in their policies to build trust with users. The future of virtual assistants might also be tied to the metaverse, with companies exploring new ways of integrating these assistants into virtual reality environments.

The emergence of ChatGPT, an intelligent virtual assistant and AI-powered language model developed by OpenAI, has also raised discussions about the future of virtual assistants. While there has been some speculation about the potential influence of ChatGPT on the virtual assistant market, it's not yet clear how it will ultimately affect the future and direction of the industry.
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The vigorous development of multimedia technology and network technology has brought modern methods to English learning, and the development of digital language systems has created a vibrant learning environment. This article aimed to study how to build a learning center system based on artificial intelligence technology to improve the ability of English language autonomous learning. Based on artificial intelligence technology, this article proposes how to build a learning center system and how to apply artificial intelligence technology to improve the ability of autonomous learning. According to the experiments in this article, in the case of traditional textbook learning, the probability of autonomous learning is 35%. In the case of artificial intelligence technology systems, the probability of autonomous learning is 53%, which is 18% higher than traditional learning. From this article, it can be seen that the proportion of students who like to use artificial intelligence technology to learn independently is about 70% and that of students who like to study with textbooks is about 30%. The experimental results show that the learning system built by artificial intelligence technology has improved people's autonomous learning ability. In short, it is necessary to expand the application fields of artificial intelligence as soon as possible in the autonomous learning of English. To improve the autonomous learning system, the power of technological changes brought about by artificial intelligence is also necessary.

1. Introduction

With the continuous improvement of artificial intelligence technology theories and methods and the booming development of English language autonomous learning and computers in recent years, autonomous learning capabilities have been greatly improved. As one of the most developed information technologies in modern times, artificial intelligence has also made epoch-making progress in the development and practice of intelligent education systems.

With the economic globalization and technological informationization of the times, the knowledge of students also needs to be continuously updated. It is impossible for a student to receive education only in school throughout his life. Only by learning the strategy of independent learning and mastering the methods of independent learning, they can achieve the goal of education. This article emphasizes the improvement of English learning effects by strengthening the integration of artificial intelligence technology and English courses. The combination of artificial intelligence and English subjects will bring better results.

With the continuous deepening of education reform in the education stage, the society has also put forward new requirements for the ability of autonomous English learning. The purpose of Sakamoto and Tsuruta's research is to clarify the effect of the explanation of the effectiveness of online self-study English on learners' consciousness and behavior through experiments. The experimental process is divided into three steps: first, in the case of searching through the Internet, it is usually more beneficial to use English keywords than to explain to learners that only Japanese keywords are used. Second, in the implementation of self-study programs, learners are restricted to use English materials. Finally, to achieve different self-learning, this time, they are only allowed to use textbooks. While conducting the experiment, the consciousness and behavior of the subjects were also investigated. The results show that although learners understand the benefits of using English keywords for Internet search, they are hesitant to use this method [ 1]. Rubaai and Young want children to learn new words by themselves. He investigated the usability, coherence, and frequency of words in textbooks and then investigated whether children paid attention to these words. He investigated and analyzed 50 children. Less than half of them noticed these words and learned them independently. The results showed that the children's autonomous learning ability was relatively poor [ 2]. Lian et al. found that to make English learners improve their learning efficiency, students' ability of autonomous learning is required to be improved. To understand whether students are autonomous in English learning, he investigated 529 college students' views on English autonomous learning. According to the survey results, college students independently carry out meaningful language learning through technology in online English courses. The structural equation model reveals that real language learning, autonomous learning, and English self-efficacy complement each other [ 3]. Genç et al. found that learners' autonomous learning ability and English learning attitudes have a great influence on education. Taking into account the important influence of autonomous learning on language learning, his research emphasizes that the learner's language learning attitude is inseparable from its autonomous ability. He surveyed 210 undergraduate English majors. He conducted a quantitative analysis of the collected information. According to the experiment, the students' English autonomous learning ability is very general, because they believe that motivation factors are more important than autonomous learning ability. In addition, it is hoped that teachers and students can improve students' autonomous learning ability, to achieve the purpose of stimulating their motivation to learn [ 4]. Chen Hsu has been studying the impact of virtual reality on student autonomy. His research is to investigate students through the English mobile learning application of VR games he created. Through this software, you can learn about students' English learning efficiency, student participation rate, and self-learning information. A total of 274 students were surveyed. Experiments show that game participation and game experience are affected by self-efficacy. During the game, students involuntarily learn new knowledge. Students' self-regulation in the learning environment is at an intermediate level. The results show that the interactive features of VR applications and the challenges of gamification design enable students to improve their autonomous learning ability. It can be seen that the new intelligent program is conducive to improving students' autonomous learning ability [ 5]. Jozwik Cuenca-Carlino found it difficult to construct the written text in English. Therefore, effective teaching needs to be developed and deal with the challenges brought by writing tasks. Scholars have explored the advantages and disadvantages of using self-regulation strategies to formulate writing guidance. Teachers must also design target-oriented English education programs starting from the development needs of students' core competitiveness. After changing students' inherent impression of English subjects, students are actively guided to integrate into the classroom, and a good habit of independent learning is gradually developed. Effective training will help students achieve the goal of accumulating English knowledge, effectively make up for defects based on English, and have an irreplaceable impact on the development of future learning [ 6]. Hong investigated English learners' autonomy and word use. He analyzed whether there was a correlation between autonomy and word use. He investigated the information of 198 college students. Finally, it is found that college students' autonomous ability in English learning is still very strong. Compensation and social and metacognitive strategies are the most important parts. He suggested that teachers improve students' autonomy through strategic guidance [ 7]. Tai and Ting found that with the popularity of multifunctional intelligent tools, they also play a great role in English learning. His survey found that students complete language learning by making English videos using intelligent technology tools. Finally, it is found that young people like to learn English in this form. They welcome and care about this happy way of learning [ 8]. Through the experimental research of scholars, we can know that the students' English autonomous learning ability will be improved with the use of artificial intelligence technology. How to scientifically use artificial intelligence technology to improve students' English learning autonomy is a problem that needs to be studied today.

The innovations of this article are as follows: (1) how to integrate artificial intelligence technology into the design of the English autonomous learning system when studying the English autonomous learning system, so that the designed system can play the greatest role. (2) This article applies the neural network algorithm to the construction of the English autonomous learning system and also found that the artificial intelligence technology and the English autonomous learning system are interrelated. Through experiments, a more reasonable design of an autonomous English learning system is obtained.

2. Deep Learning Method Based on Artificial Intelligence

Artificial intelligence is a new technological science that studies and develops theories, methods, technologies, and application systems used to simulate, extend, and expand human intelligence [ 9]. Its practical applications include machine vision, fingerprint recognition, face recognition, retina recognition, and iris recognition. The visual mechanism of the human brain enlightens that human beings are faced with a large amount of perceptual data at all times, but the brain can always capture important information easily. The core problem of artificial intelligence is to imitate the brain's ability to represent information efficiently and accurately [ 10, 11]. Deep learning originated from artificial neural networks [ 12]. It is influenced by biology and imitates the information processing mechanism of the human brain to analyze images, sounds, texts, and other data [ 13]. With the development of science and technology, as well as the advancement of GPUs and other hardware devices, the processing speed of deep learning has accelerated, and the real-time performance has been greatly improved. Therefore, the target detection algorithm designed in this study is based on the convolutional neural network structure [ 14], as shown in Figure 1.

As shown in Figure 1, artificial intelligence is the most advanced information technology. Artificial intelligence promotes the development of education. It also promotes the autonomous learning of English and lays a rich theoretical and practical foundation for the improvement of students' ability [ 15].

2.1. Convolutional Neural Network Algorithm

Convolution operation refers to opening an active window with the same size as the template starting from the upper left corner of the image, and the window image and the template pixels are multiplied and then added. The calculated result is used to replace the pixel brightness value at the center of the window. Object detection uses image processing technology, artificial intelligence, deep learning, computer vision, and other fields of technology and theories to achieve object detection and recognition, category determination, and position calibration [ 16]. It is the premise of object recognition and high-level semantic analysis [ 2]. Because of its strong ability to learn image features, deep convolutional neural networks have been proven to have certain advantages for object detection problems, as shown in Figure 2.

As shown in Figure 2, with the development of deep learning theory and its mature application, it has greatly improved the performance of object detection. The object detection framework has also developed from a traditional framework to an object detection framework based on deep learning [ 16, 17]. Deep learning, also known as machine learning technology, is a way for computers to learn, predict, and classify information through layer filtering input. At present, the object detection framework based on deep learning consists of two processes: model offline training and model online reasoning. It mainly includes six parts: image preprocessing, region extraction algorithm, feature extraction model, classifier, regression, and non-maximum suppression [ 18].

A convolutional neural network is a neural network that is based on convolutional layers and contains multiple types of network layers [ 19]. Its basic structure is shown in Figure 3.

As shown in Figure 3, using neural networks to process intuition and image thinking information can get better results than previous processing methods. The development of neural networks has a very broad scientific background and is a comprehensive result of many fields. The study of neurons and artificial neural networks in the human brain may produce a new generation of artificial intelligence machines, that is, neural computers [ 20]. Now, neural networks are widely used in pattern recognition, image processing, combination optimization, automatic control, information processing, robotics, artificial intelligence, and other fields [ 21, 22].

The convolution operation of the convolution layer is divided into continuous convolution and discrete convolution. The continuous convolution operation formula is as follows:

The discrete convolution operation formula is as follows:

The convolution operation of the convolutional neural network is a discrete convolution operation, but it is somewhat different from the definition of analytical mathematics [ 23]. The formula of discrete convolution operation is expressed as follows:where is the gray value of a row and b column in the image; is the convolution kernel or filter. The convolution diagram is shown in Figure 4.

As shown in Figure 4, the image size is 6 × 6, and the weight of the folded core is 3 × 3. The weight must pass through the entire image and cover all pixels at least once, and the given output is the extracted feature. The features extracted by multiple convolution kernels are different, one is to extract edges, the other is to specific colors, and the other is to eliminate unnecessary noise. In most cases, this is not a single weight matrix, but a combination of multiple filters of the same size [ 24].

Generally, to reduce the spatial size of the image, a downsampling layer is periodically imported between the convolutional layers. This is done independently in each depth dimension, so the depth of the image will not change. The most common form is maximum downsampling. Of course, other forms such as average sampling can also be used [ 25]. The algorithmic expression of average downsampling is as follows:

The algorithmic expression of the maximum downsampling is as follows:where F represents the feature map matrix; S represents the obtained downsampled feature map; C represents the moving step size; and represents the largest element taken from the area of size in the input feature map F [ 26].

2.2. Logistic Regression and Softmax Layer Method

The concept of logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model, often used in data mining, automatic disease diagnosis, economic forecasting, and other fields. Logistic regression is essentially a binary classification problem. A logistic regression algorithm is a classification algorithm, and the output value range is as follows:

Assume the function is as follows:

The model predicts y  = 1, and what the logistic regression algorithm needs to perform is to minimize the cost function through model parameters, as shown as follows:

The logistic regression algorithm is as follows:

Then, we get the following:

Among them, Y is the input variable; θ is the parameter.

In softmax regression, it is assumed that the form of the function () h xθ is as follows:

Activation function layer: each neuron node in the neural network accepts the output value of the previous layer of neurons as the input value of this neuron and passes the input value to the next layer, and the input layer neuron node will input the attribute value. It is the activation function. Usually, the activation function is to inject nonlinear elements into the neural network. The data are made separable under nonlinear conditions to solve the problem of poor linear expression ability of the original neural network. After the data are expressed sparsely, the efficiency of data processing is higher.

The expression of the sigmoid function is as follows:where tanh is zero mean, and values from −1 to 1 can be activated. The tanh function is shown in Figure 5.

As shown in Figure 5, the tanh function can compress the input continuous real numbers to between 0 and 1. In particular, for very large negative values, 0 is output; on the contrary, for very large positive numbers, 1 is output, but it has a fatal flaw. When the input is very large or very small, the gradient of the neuron is close to 0, which will make the network difficult to learn. The mathematical expression is as follows:

The ReLU function is shown in Figure 6.

As shown in Figure 6, obviously, it only responds to input values greater than zero. In recent years, ReLU has become more and more popular. Because using ReLU is much faster than the SGD obtained by sigmoid or tanh, the convergence speed is much faster. Moreover, it is easier to operate, and only a threshold value is needed to get the activation value. The mathematical expression is as follows:

The dropout layer can speed up the calculation, avoid overfitting, and be more robust. As the name implies, a certain number of neurons in the hidden layer are randomly discarded during training, as shown in Figure 7.

As shown in Figure 7, the dropout layer can solve the problem that all nodes are activated when the convolutional neural network analyzes the image.

The iterative process of convolutional neural network is mainly divided into two steps.

2.2.1. Forward Propagation

Taking the neural network model in Figure 7 as an example, the forward propagation process of the neural network is shown as follows:

The input weighted sum of unit A of layer Z is as follows:

Then, the activation value of the Ath unit of the Z layer is as follows:

SPPNet proposes the structure of the image pyramid, as shown in Figure 8.

As shown in Figure 8, first, the convolutional neural network is used to extract features from the entire image. Each selected region in the image corresponds to a different region on the feature map, and then, these regional features with different scales are converted into feature vectors of the same size through a pyramid pooling operation and then input into the classification layer. In the above method in which convolutional features are shared by multiple regions, the calculation speed is greatly improved.

2.3. Antimodel Training Overfitting Technology Method

The reasons for the model overfitting mainly include three aspects: the data set is large enough, but the data quality is poor; the data set samples are too few; and the machine learning model is too complex. The problem of poor data quality can be dealt with by ways of improving data quality. Too few data set samples are often processed by data augmentation techniques. If the machine learning model is too complex, regularization technology is used to improve it. In addition, the method of combining the validation set and terminating the model training early can also solve the model overfitting to a certain extent. Next, we will briefly introduce the anti-model training overfitting technology.

2.3.1. Regularization Technology

Given the data set then the average loss of the machine learning model on the data set is defined as as follows:

However, in reality, the limited training data often lead to the problem of model overfitting. Therefore, the regularization technique is as follows:

This article also uses the average mean square error to evaluate its performance, which is defined as follows:

Assuming that the pixel coordinate of the center of the human head object in the image is , then the human head object represented by this point can be represented by the impulse response function . Then, the number of heads contained in the whole image is as follows:

The research and practice on the application of artificial intelligence in education started very early, and the scope of attention of educational technology researchers is also very wide, for example, computer-aided education, intelligent robots, and other artificial intelligence-related technologies. Artificial intelligence-related technological achievements are introduced into the actual education and learning process to improve education. In the connection of many educational activities, scholars in related fields have paid attention. These all reflect the various developmental characteristics of artificial intelligence-related research content.

3. Experiment of Neural Network and Questionnaire Survey

3.1. Experiment of Questionnaire Survey

There are various problems in the current English computer-assisted learning, such as focusing on comprehensive application and not focusing on basic knowledge. Students use computers to store a large amount of information to improve their comprehensive application ability. When some students face a large number of applications, the lack of basic knowledge affects students' understanding and acceptance of the form of attention. This method ignores how to improve students' autonomous learning ability.

With the rapid development of social economy and the wave of mobile Internet, more and more smart terminals appear in people's daily lives. Many smart apps can provide users with correct pronunciation for practice. Users can turn on the APP microphone and recording function, use the APP to turn on the microphone, practice pronunciation, and read articles. APP uses voice recognition technology to analyze the user's pronunciation and other aspects and provide users with the correct pronunciation for practice. Users can also practice listening through the APP, and they can choose random practice according to their own ideas or practice according to the scene and theme. The exercise was divided into two parts: one was to repeat what was heard, and the other was to write down what was heard. Each listening practice will be gradual, increasing the speed of speaking and lengthening the length of sentences. This study investigates the autonomy of 50 students in textbook learning and the autonomy of learning with smart APP, and the results obtained are shown in Figure 9.

As shown in Figure 9, the lowest autonomy of 50 students learning through traditional textbooks is 13.5%, and the highest is only 34%, while the lowest autonomy of learning through smart apps is about 34%. The highest is 70%, which is 36% higher than the autonomy of learning through traditional textbooks. It can be seen that through various intelligent technologies, the autonomy of English learning will be much higher than the autonomy of traditional learning methods.

This article conducts a survey of ten students who study independently through traditional textbooks, as shown in Table 1.

As shown in Table 1, 10 students learn independently through traditional textbooks, and their interest in learning is very low, with an average of about 4.5 points. This also leads to very low learning efficiency, with an average of about 4 points, and none of them reach the pass line.

This article then conducts a survey on ten students who learn autonomously through artificial intelligence, as shown in Table 2.

As shown in Table 2, under the influence of the artificial intelligence technology learning system, students are more and more interested in English learning, generally reaching a score of 7 or more. In other words, more and more applications of artificial intelligence technology in English autonomous learning systems are loved by people, and the field of information technology is also constantly improving. The interaction between people and machine terminals is no longer limited to clicks and touches. With the rapid development of social economy and the wave of mobile Internet, more and more smart terminals appear in people's daily lives. At present, ordinary click-and-touch interactions can no longer meet people's needs, so through the application of voice interaction technology, people's modern needs can be better met. This article surveys 10 students on traditional learning and artificial intelligence learning, as shown in Figure 10.

As shown in Figure 10, through the analysis of 10 students, it is not difficult to find that blindly using textbooks for learning is obviously not suitable for the current learning needs. To effectively improve the self-learning ability of students, it is necessary to strengthen the combination of artificial intelligence technology and English courses. In the autonomous learning of English, the application of artificial intelligence is undoubtedly a high-tech, novel, and effective way.

3.2. Experiment of Neural Network Method

To display the feature maps extracted by each layer of the network more intuitively, this section introduces the method of neural network feature visualization and analyzes the obtained features. To analyze the influence of the feature extraction network structure on the target detection effect at the same time, the data set after data enhancement is used to train the entire target detection model using ZF-Net and VGG-17 as the feature extraction network. For the average accuracy of different iteration times, the data records are shown in Table 3.

It can be seen from the data in Table 3 that the average accuracy rate of the model trained with the original data set has increased by 2% with the enhanced data. From the perspective of each category, the accuracy of each category has also been improved by about 2%. It can be said that the quality of the data set has a greater impact on the training results, which is also an important reason why deep learning algorithms need to pay attention to the data.

According to the three themes of the artificial intelligence course, this article conducts an investigation of the learning mode, and the main purpose is to understand the learning mode that students think the artificial intelligence course should adopt. At the same time, it also examines the students' understanding of each learning mode, to provide a factual basis for the learning mode that should be adopted in artificial intelligence courses. The data obtained from the survey are shown in Table 4.

It can be seen from Table 4 that problem-based learning mode, case-based learning mode, inquiry learning mode, and information processing learning mode are the learning modes that students are more familiar with. In the study of the topic of "knowledge and its expression," students tend to learn case-based learning mode and problem-based learning mode; in the study of the topic of "reasoning and expert systems," students tend to study based on question, cognitive apprenticeship, and information processing; and in the study of the topic of "artificial intelligence language," students tend to study based on question, introduction, and case-based learning.

This article investigates and analyzes the students' familiarity with artificial intelligence and textbook learning, and the results obtained are shown in Figure 11.

As shown in Figure 11, students are generally in a state of general understanding of the content of the curriculum standard. Among them, students have the best mastery of reasoning and expert system topics, the least mastery of knowledge and expression topics, and the intermediate level of mastery of artificial intelligence language topics. This shows that students are relatively familiar with the content of artificial intelligence language, which provides a more favorable condition for language-based artificial intelligence learning.

4. Discussion

This article explains the basic principles of neural network algorithms and introduces convolutional neural network algorithms in detail. At the same time, it also introduces the theoretical basis of the anti-model training overfitting technique and provides some commonly used methods. The deficiencies are learned, and the convergence of the algorithm is theoretically proved.

This study analyzes the research progress of artificial intelligence technology in English language autonomous learning, elaborates the related concepts of artificial intelligence technology and English language autonomous learning, researches the relevant theories of English language autonomous learning based on artificial intelligence technology, and explores how to improve the ability of autonomous English learning based on artificial intelligence technology, and through the analysis of the questionnaire survey to discuss the importance of artificial intelligence technology to the autonomous learning of English language, and finally takes the integration of artificial intelligence technology into the autonomous learning of English language as an example to explore the relationship between the two.

This article also makes reasonable use of the convolutional neural network algorithm. With the increasing application scope of convolutional neural network algorithms and their importance gradually becoming more prominent, many scholars have begun to match certain specific algorithm theories with real-life application scenarios and propose feasible algorithms. According to this algorithm, artificial intelligence technology is an indispensable part of improving English autonomous learning.

5. Conclusions

This article mainly starts from artificial intelligence technology and English language autonomous learning and discusses the relationship between the two and how to integrate artificial intelligence technology into English language autonomous learning. Based on the convolutional neural network algorithm, the importance of artificial intelligence technology in the application of English language autonomous learning can be learned. Artificial intelligence technology is indispensable for autonomous English language learning, but we should not imitate others blindly. Based on the application of artificial intelligence technology in English language autonomous learning, the relevant scientific fields involved in the research are relatively extensive, and the concept of artificial intelligence technology has always been disputed. The author is not talented, and his knowledge of the world is still shallow. The academic theory and professional ability are relatively weak, and there are inevitably shortcomings. There are still many shortcomings in the design work, and the author is constantly making progress, hoping to do the best.

Data Availability

No data were used to support this study.

Conflicts of Interest

The author declares that there are no conflicts of interest.

# data[].attributes.attachment_link.title
Construction of English Language Autonomous Learning Center System Based on Artificial Intelligence Technology

# data[].attributes.attachment_link.description
The vigorous development of multimedia technology and network technology has brought modern methods to English learning, and the development of digital language systems has created a vibrant learning environment. This article aimed to study how to build a learning center system based on artificial intelligence technology to improve the ability of English language autonomous learning.

# data[].attributes.attachment_link.readable_text
Abstract

Artificial intelligence has replaced simple repetitive mental work, and the traditional social employment system and occupational forms have undergone profound changes. This paper mainly adopts the literature analysis method, that is, the method of scientific understanding of the research objects through collating, analyzing and studying the collected literature data. According to the different contents of the theme, the frequency statistics and descriptive statistics of the problem are carried out, and the analysis results are given in the form of text, tables, statistical charts, etc. A total of 198 questionnaires were distributed, and the effective recovery rate of 190 valid samples was 95.9%. Experimental results show that 79.5% of teachers understand artificial intelligence through popular science books and news events. Through teacher training, teachers' understanding of artificial intelligence reached 13.4%.

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References

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Authors and Affiliations

Department of Economics and Management, Luoding Polytechnic, Luoding City, Guangdong Province, China

Zhiquan Hu

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Correspondence to Zhiquan Hu .

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Editors and Affiliations

1. Distributed System and Security Research Cluster, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

Prof. Jemal H. Abawajy

2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

Kim-Kwang Raymond Choo

3. Shanghai University of Medicine and Health Sciences, Shanghai, China

Prof. Zheng Xu

4. University of Oklahoma, Norman, OK, USA

Prof. Mohammed Atiquzzaman

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Hu, Z. (2021). Influence of Introducing Artificial Intelligence on Autonomous Learning in Vocational Education. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_54

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This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

1. Introduction

AI has been increasingly propagated as having strategic value for education [ 7]. Loeckx [ 8] suggested that AI could be an effective learning tool that lessens the burdens of both teachers and students and offers effective learning experiences for students. Coupled with current education reforms such as the digitalization of educational resources, gamification, and personalized learning experiences, there are many opportunities for the development of AI applications in education. For example, the modelling potential of AI techniques has been exploited systematically to develop reactive and adaptive tutorials for the construction of individualized learning environments as compensation for the shortage of teachers through the use of intelligent tutoring system (ITS) [ 10]. ITSs provide personalized learning experience in four main ways: monitoring student's input, delivering appropriate tasks, providing effective feedback, and applying interfaces for human-computer communication [ 7]. When more ITSs are created for more subjects and topics, it is likely to change the role of teachers, and hence, schooling may need to be reconceptualized. There exist many concerns and worries among teachers on if AI challenges their jobs. At the same time, such questions as what is being learned and how AI is being used are being discussed currently by researchers as well as by educational practitioners. Some researchers wondered whether advancements in AI would challenge or even replace teachers since many other jobs are being replaced by automation [ 11]. There is an emerging recognition that teachers' professional roles need to be adjusted as AI advances and this will trigger new organizational forms [ 12]. Emerging challenges also included students' attitudes towards these changes [ 13]. To some extent, students as digital citizens are able to leverage AI to improve learning outcomes. Nonetheless, they may fail to use suitable AI techniques appropriately for a specific learning context, which would result in negative attitudes towards learning [ 14].

To summarize, this research involves a review of the studies of AI in education. Previous studies have included three essential perspectives of AI in knowledge processing: (a) knowledge representation, (b) knowledge obtaining, and (c) knowledge derivation [ 3]; this review will focus on AI techniques and tools that have been integrated into education recently after the proliferation of AI. The "first generation" of AI could support human intellectual work by applying rule-based expert knowledge, and the "second generation" may find the optimal solution by statistical/search model, while the "third generation" will dramatically improve recognition performance based on the brain model. This review focuses on articles published in the period from 2010 to 2020 from the Web of Science, as that represents the period when the second and third generation of AI began to make headways into education. The research questions that guided this review are as follows:(1) What is the overall state of AI in education? Which research topics and research designs related to AI in education are evident from 2010 to 2020? (2) What are the trends in published studies in terms of AI in education? (3) What are the challenges generated from the current research of AI in education?

2. Method

This study is a systematic literature review. The objectives of the review were to analyze and interpret findings based on predefined research questions (see above) and criteria which serve to point out future directions [ 15]. The predefined research foci as shown in Table 1 are research purpose, learning subject, educational level, research approach, and effects. The review was conducted in three stages: planning, performing, and reporting the systematic review.

2.1. Planning the Review

As previous reviews about AI were conducted in the physical sciences [ 16, 17], the study aimed to conduct a review in the field of the social sciences.

The Web of Science database and the Social Science Citation Index (SSCI) journals were selected for the search for desired articles published from 2010 to 2020. Articles published in the SSCI database are generally considered as high-quality publication among education researchers. The keyword employed was "artificial intelligence," and the subject area was refined to "education and educational research". This process yielded 142 articles including 121 research articles, 10 review papers, one interview paper, and 5 book reviews. The selected articles include both analytic studies (primarily qualitative research) and empirical studies (primarily quantitative research).

2.2. Performing the Review

Following Wu et al. [ 18], this study was conducted in two steps: identification and coding. In the first step, an article was selected to the potential pool when it qualified for either of two criteria: (a) the research involved a specific AI technique as an intervention in assisting learning or teaching and (b) it provided empirical evidence or in-depth analysis. As already noted, only articles indexed in SSCI were considered. It should be noted that studies that focused on the development processes of AI without educational implications or only adopted AI as a learning subject without the employment of AI were excluded from this review. Second, as for the analytic studies, only studies that discussed the effect of AI techniques on education were included. Each full text of all the identified papers was read and screened individually by three-panel members with doctoral degrees or professorships in the field of learning technology. Studies that did not fit clearly with the criteria were brought up for panel discussion. The screening process yielded 100 articles out of the original set of 121.

In the second step, all the authors discussed thematic analysis principles and established a coding scheme in terms of how AI was used in education. Two main categories were investigated: research questions and technology adoption. Firstly, with regard to research questions, previous research has found three basic models of AI in knowledge processing: knowledge representation, knowledge obtaining, and knowledge derivation [ 3]. Building on that foundation, the research questions of the sample papers were classified into three dimensions: (a) development, focusing on the knowledge presentation model; (b) extraction, centering on how to obtain knowledge from data mining; and (c) application, emphasizing the human-computer interaction through information derivation. Secondly, with regard to technology adoption, the focus was on the types of technology that the study adopted, which were further categorized into software (e.g., algorithms and programs) and hardware (e.g., sensors and devices such as virtual reality). It should be noted that a study with technology without an AI purpose in education was not included. A detailed description is shown in Table 1 and it includes learning subject, educational level, research approach, and effects. Moreover, the researchers conducted further frequency comparisons on the associations between the research purposes and some factors such as AI technology adoption as well as time periods to predict the trends and challenges of AI in education.

3. Findings and Discussion

According to the above coding criteria and content analysis, the three dimensions of research questions are shown in Table 2 and the 72 studies from 63 empirical studies (5 papers have two studies and 2 papers have three studies) are further subclassified into 11 categories. There are 23 studies in the dimension of development. The AI technique was utilized as a development tool for the construction of a smart learning environment, which can be subclassified as focusing on the development of algorithms including classification, matching, recommendation, and deep learning for teaching and learning purposes. Additionally, 35 reviewed studies were found in the dimension of extraction, which referred to the application of developed AI techniques, normally based on algorithms, to offer students feedback, reasoning, and adaptive learning. 14 empirical studies were found in the dimension of application which consisted of affection computing, role-playing, immersive learning, and gamification. In the integration dimension, AI techniques included those involving human factors as vital variables to identify and analyze learners' personalized features. In such studies, human-computer interaction was generated to improve such characteristics as creativity, responsibility, and critical thinking that can impact learners' performances and perceptions. The following sections describe what educational issues were dealt with in the age of AI and how AI technique was employed in each research question.

3.1. Dimension of Development

As shown in Table 2, 16 empirical studies were found focusing on the development of education systems such as intelligent tutoring system (ITS) and electronic assessment. The development procedure was usually conducted with an induction-deduction approach, in which prior experiments and data were analyzed to predict the variables followed by the algorithm testing to obtain the final modelling equation [ 19].

Additionally, in terms of the matching/group formation modelling, prior research employing stereotype theory has assessed that the Bayesian networks, association rules, clustering, fuzzy C-means, and the fuzzy and genetic algorithms were well-accepted algorithms for the modelling of individual properties of the student. These techniques provide potential indications for the investigation of forming homogeneous and heterogeneous groups in an educational context [ 21].

Moreover, the trends of the growing amount of data challenge educators to analyze qualitative data efficiently. Natural language processing (NLP) provided a means to diagnose the problem and make a recommendation by simplifying and accelerating the discovery of what lies within the data [ 22]. However, the assessment of a complex educational system requires more profound information retrieval. The integration of multiple approaches, such as benchmark in NLP/Semantic Web field, was suggested to model smarter computer-aided systems in which agents could be trained automatically [ 23].

To optimize the modelling in the learning context, the hierarchical structures were considered as potential solutions to model the educational system. This is because education is generally a complex system with the exhibition of subsystems and components, in which the invisible causal processes among subsystem/component behaviours would causally affect each other [ 24]. It was suggested that systematic modelling should analyze three dimensions in the education context: learner's variation, learning domains, and learning activities [ 25, 26]. For example, some researchers constructed the higher-order item response theory framework involving the overall ability at the first dimension and multiple domain abilities at the second dimension, which has been well adopted in the automatic problem-solving process [ 27].

Based on the above and Nguyen and Yang's suggestion [ 28], the aims of developing an AI-integrated system in education could be grouped into four types: classification (5 studies), matching (3 studies), recommendation (5 studies), and deep learning (10 studies). (1) Classification refers to the reconstruction of knowledge bases, in which the materials could be categorized according to varied characteristics. Classification demarcates knowledge content, which contributes to the accuracy of text analysis [ 3]. For example, some researchers developed an ITS with the characteristics of categorizing motion problems, by which learners could easily access different types of motion problems in Mathematics [ 29]. (2) Matching refers to a conversion mechanism, in which varied sets of classification are connected to specific learning purpose. For example, a text-to-diagram system was developed for blind students to link geometry words to an underlying diagram on the Braille printout, which has been certified as an effective teaching/learning tool at a Blind school [ 30]. (3) The recommendation is regarded as an intelligent authoring tool. With the support of the natural language process, it could automatically create new themes, theories, and pedagogical contents as a response to learners' feedback, to help teachers save time and effort [ 31]. It constructed a human-computer interaction and widely used to generate real-time and intelligent feedback according to learners' input, which has been regarded as a reliable feature in modern assessment system [ 32]. (4) Deep learning, or machine learning, is a comprehensive approach of big data processing and learning behaviour analysis. Based on the proliferation of big data in education, such as learning or teaching behaviour, the system could self-adjust to meet users' dynamic requirements by upgrading its algorithms [ 33].

To date, some studies have reported the lack of significant impact on improving teaching. The challenge was largely attributed to the weak pedagogical design and lack of appropriate assessment criteria [ 8]. Future research should therefore be grounded in learning theories so that more acceptable, accessible, and efficacious AI can be an integral part of learners' lives.

3.2. Dimension of Extraction

Educators have begun to explore suitable applications of AI techniques in their teaching. There are currently some AI applications that have achieved the integration of technique, domain knowledge, and pedagogical design. The three types of pedagogical applications of AI identified in this review were feedback (16 studies), reasoning (10 studies), and adaptive learning (9 studies). While these applications could be interlinked, they were categorized as such based on the classification explicated by the authors of the reviewed articles.

3.2.1. Feedback

One of the challenges impairing personalized learning is the inappropriate sequencing of contents. The restructuring of presentation sequences is seeking a way to redefine the organization of knowledge according to the student's reaction. In this situation, feedback is an important approach to meet learners' proximal learning patterns [ 9]. Using an artificial neural network, the system provides immediate feedback according to students' input to help them gradually get access to the abstract concepts and perform practical exercises. Besides, researchers perceived a positive trend towards the system, which may attribute to two perspectives.

Although a large number of benefits were reported with respect to automated feedback of domain knowledge, no research in this review had established the connection to pedagogical theories. Most of the authors in the development dimensions were from the computer science domain, which leads to their focus on the presentation of source data (domain knowledge) technically without much pedagogical consideration.

3.2.2. AI-Supported Reasoning

The recursive feedback may have the potential to foster learners' abilities to reason in specific ways because the human-computer interaction is able to engender among the students a sense of responsibility toward improving the construction of knowledge repository [ 39]. The reconstruction of the knowledge repository was seen as a process of using modelling to realize pedagogical design as shown in Figure 1(b). However, some researchers found that novices such as students and preservice teachers showed minimal understanding of the invisible causal behaviours in the system compared to experts and experienced teachers [ 24]. Another research showed a similar conclusion: students were able to learn the relevant facts and pairwise relations, while they may still fail to reason with them very well [ 39]. One possible explanation could be that reasoning is largely invisible and it is difficult to induce the processes of reasoning through the observation of the behaviours. AI techniques such as the visualization technique could be applied to foster learners' reasoning.

To help learners improve their reasoning, the graph structure [ 29] and learners' engagement [ 24] techniques have been studied. For the graph structure, intelligent systems could be developed to make thinking visible. In a sense, the simulation approach of the AI technique was employed to mimic thoughts tracking the reasoning visually in real time. For example, the argument-mapping tools were designed to assist learners with visualization of the premises and conclusions of arguments. The findings showed that a sequence of connected arguments was chained together for learners to make an ultimate conclusion [ 40]. Drawing from the sociocultural theories of learning in designing AI to support students' reasoning, Vattam et al. [ 24] reported that engaged learners could better understand the multiple levels of organization in complex systems. Therefore, students' engagement is an essential aspect to be considered for the design of a learning system that aims to support reasoning.

However, using AI techniques, including visualization and hierarchical reasoning modelling, may be inadequate to support reasoning. The four studies reviewed focused on the utilization of modelling to support general reasoning, while the reasoning model should be largely domain-specific [ 24, 39, 40, 42]. Moreover, there is an unresolved challenge in coding learners' behaviours as far as AI-supported reasoning is concerned. The reasoning process may be more effective when learners' personalized performance is considered. Although the visualized reasoning tools could perform well in a small-scale group setting, it is difficult to obtain adequate reasoning analysis of the data from a large population because the reasoning system fails to adjust itself automatically. Therefore, the requirements of dealing with increasingly large and diverse data demand self-adaptive alternatives [ 9].

3.2.3. Adaptive Learning

Previous research has emphasized that the design dimension was a worth exploring alternative in the application of adaptive system [ 46]. To design successful adaptive systems in education, curriculum designers and system designers have to leverage on to include the modelling of the problem-solving process in the specific domain knowledge and the use of big data [ 21, 44]. Firstly, the mechanism of the adaptive system connects learners' prior domain knowledge and the evaluation of their current domain performance to scaffold their problem-solving [ 47]. In particular, the pedagogical design is essential in adaptive intelligent context. It involves the selection of adaptive algorithms and considerations about the compatibility of the learning style and the intelligence supportive methods. In this sense, the assumption that AI would threaten the teachers' position may be unfounded because of teachers' vital role as curriculum designers. Secondly, the adaptive system is empowered by big data. Since the main feature of the adaptive learning system is personalization, accumulation of big data such as the range of diverse individual characteristics and learning style and preferences is necessary for intelligent personalization to be realized. However, research on personalization in the context of the adaptive system is limited to the users' characteristics related to domain knowledge. The deeper internal characters, such as human mental status and creativity, were barely noticed and studied [ 21]. This however has vital research potential with the development of advanced AI techniques such as biofeedback techniques.

3.3. Dimension of Application

3.3.1. Technology Adoption in the Application Dimension

According to the content analysis of the selected papers shown in Table 1, there are five typical AI techniques that supported affection computing and analysis in the education sector. They are complex algorithms, visualization, XR (virtual/augmented/mixed reality), wearable technique, and neuroscience. In many situations, they supported each other to construct a smart learning environment and system. (1) Complex algorithms were designed with consideration of human factors rather than the simple combination of functional blocks. From the perspective of human-computer interaction, the learners should be treated as a knowledge creator rather than the receiver, which helps to generate positive affection status. From the perspective of presentation modes, the traditional declarative statements in a computer system should be replaced by more diversified verbal presentations such as dialogue, coaching, and generality. (2) Visualization was seen as an optimal method chosen for the solution of complex conception. One of the benefits of visualization is making complex knowledge entertaining, such as game-based learning, in which learners' motivation will be greatly generated. (3) XR including virtual/augmented/mixed reality provides a highly simulated learning context, which may be challenging to realize in physical classrooms. For example, to help learners understand complex landforms in geography, XR indulges students into a lively and creative status. (4) The wearable technique, such as Google glasses, helps to integrate learning activity into somatosensory moves. Although it was still in an exploratory period, it has great potential to advance domain knowledge in a practical context in daily life. (5) Modern neuroscience exploits how the brain works and this expands the research of learning to include the learners' physiological state. Research in this area would enrich understanding about individual variations and could provide additional avenues to match instruction with the most optimal guidance.

3.3.2. The Categories of the Application Dimension

With the supports of the above five AI techniques, four types of learning models were generated with the application of affection analysis, which was biofeedback (6 studies), role-playing (2 studies), immersive learning (2 studies), and gamification (4 studies).

Affection computing refers to the analysis of human emotions and feelings captured by physical sensors and affective algorithms, which has gained much attention in recent years. Affection computing enhanced human-computer interaction. Based on the facial identification, some researchers improved the intelligent tutoring system by which students' emotional status was detected to give them timely emotional feedback [ 51]. Two essential aspects are needed to optimize the affection computing technique: first, teachers have to make timely appropriate instructional adjustments according to learners' affective status; second, comprehensive operation of multimode affection sources as a single source is unlikely to provide accurate analysis of affection. For example, the eye-tracking technique could capture learners' eye fixation to track the attended area, but the reasons for the foci may be attributed to different affections such as interest, anxiety, or even distraction. An additional source of data such as EEG could help to make a more accurate assessment [ 52].

3.4. The Results from Qualitative Research

4. The Research Trends of AI in Education

4.1. Technology Adoption of Internet of Things

4.2. Swarm Intelligence in Education

4.3. Deep Learning and Neurocomputation

4.4. Evaluation of AI in Education

All empirical studies reviewed presented the positive effects of AI techniques on education (see Table 1). However, the interview and the review paper have, respectively, surfaced the challenges or misunderstanding of AI in education [ 4, 21]. There is a need to articulate a holistic evaluation criterion to measure the effectiveness of AI in education. To ensure the validity and reliability of the evaluation, a multidimensional model should be adopted, which includes technique, pedagogical design, domain knowledge, and human factors. Woolf's [ 119] Roadmap for Education Technology predicted that in the era of AI Educational Data Mining, the lifelong assessment of students' knowledge, their progress, and the environments where they learn, as well as the success and failure in teaching strategies, can be chronologically tracked.

Besides, current research is disproportionately focused on specific educational contexts and a handful of variables. As shown in Figure 2, most research sampled students as participants, while teachers and professor practitioners were less noticed; additionally, most researchers considered science, humanity, and social science as subjects, but less attention was paid to sports, arts, and special education. For example, only one study was found to develop text-to-diagram conversion as a novel teaching aid for blind learners [ 30].

5. The Challenges AI Confronted in Education

AI is a promising field that faces many technology bottlenecks. The challenges would be more complex and intricate, especially when they are connected to an application in education. The challenges this review identifies could be classified into three categories: technique, teachers and students, and social ethics.

Another great challenge reported in the Horizon report in 2018 is the reconceptualization of the role of educators. Teachers' attitudes towards AI have a significant influence on the effectiveness of using AI in education. Teachers may swing from total resistance to overreliance. The former could arise from inadequate, inappropriate, irrelevant, or outdated professional development. The latter may be due to teachers' unrealistic expectations. These teachers may focus too much on the emerging AI technologies rather than learning itself [ 44]. Additionally, from the perspective of students, AI technique may provide smart and efficient tools that cause students to avoid doing the knowledge processing work that teachers expect them to do. For example, the AI translators may offer ready-made illustrations, pronunciation, fixed phrases, and even a serial of examples. Students are thus unwilling to engage in the inquiry processes that facilitate deep learning.

6. Conclusions

Given the rapid growth of AI, there is an urgent need to understand how educators can best utilize AI techniques for the academic success of students. This paper reviewed AI in education research from 2010 to 2020. It is found that the research to date could be classified into three dimensions: the dimension of development including classification, matching, recommendation, and deep learning; the dimension of an extraction involving feedback, reasoning, and adaptive learning; and the dimension of application including affection computing, role-playing, immersive learning, and gamification. Moreover, based on the research questions and the related AI techniques, four research trends were identified. They are the Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of the effect of AI in education. The challenges of AI in education were also conspicuously seen in terms of technique perspective, teachers' and students' roles, and social ethical issues. These findings could be valuable references for educational researchers, students, and AI developers who plan to contribute to the relevant studies. Furthermore, it seems clear that educators need to work with AI engineers to address the gaps between technique and pedagogy.

7. Limitations and Future Study

Although this review does propose some valuable trends and potential research directions for AI in education, there exist several limitations. Firstly, the papers reviewed in this study were filtered from Social Science Citation Index, while other databases on natural science (e.g., SCOPUS and EI) and sources (e.g., reports, news, conference papers, and patents) could be involved to offer a more comprehensive overview in this field. For instance, articles from the International Journal of Artificial Intelligence in Education that has published 30 volumes were not considered. This review therefore is limited only to SSCI articles. Additionally, the initial search could be extended using more keywords such as adaptive learning and tutor system, which may lead to the latest technical reports of AI in education that were not included in this paper. Secondly, since the current review was not attempted to be inclusive but to provide a systematic overview of AI in education, the analysis in this review may provide a framework for future research integration. For example, a more formal meta-analysis could be conducted on selected empirical studies that reported effect sizes to see what impact on learning AI might be having. Besides, the future analysis could go back further in time to see if there were changes about the time that AI 2.0 started to make headways into education.

Data Availability

The content analysis data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Acknowledgments

This research work was supported by the 2020 Humanities and Social Science Projects of the Ministry of Education (Grant ID: 20YJC880118), National Science Funding of China (Grant ID: 61977057), 2019 National Social Science Funding of China (19ZDA364), and the project of Informatization Capability in University Governance System, Chinese Association of Higher Education, 2020 (Grant no. 2020ZDWT18).

# data[].attributes.attachment_link.title
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

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This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education.

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Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, Taiwan

National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

School of Dentistry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada

Graduate School of Education, Rutgers - The State University of New Jersey, New Brunswick, NJ, United States

Apprendis, LLC, Berlin, MA, United States

Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Central University, Taoyuan City, Taiwan

Graduate School of Informatics, Kyoto University, Kyoto, Japan

Department of Electrical Engineering, College of Technology and Engineering, National Taiwan Normal University, Taipei, Taiwan

Centro de Tecnologia, Universidade Federal de Santa Maria, Santa Maria, Brazil

Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan

We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

Introduction

The purpose of this position paper is to present current status, opportunities, and challenges of big data and AI in education. The work has originated from the opinions and panel discussion minutes of an international conference on big data and AI in education ( The International Learning Sciences Forum, 2019), where prominent researchers and experts from different disciplines such as education, psychology, data science, AI, and cognitive neuroscience, etc., exchanged their knowledge and ideas. This article is organized as follows: we start with an overview of recent progress of big data and AI in education. Then we present the major challenges and emerging trends. Finally, based on our discussions of big data and AI in education, conclusion and future scope are suggested.

Rapid advancements in big data and artificial intelligence (AI) technologies have had a profound impact on all areas of human society including the economy, politics, science, and education. Thanks in large part to these developments, we are able to continue many of our social activities under the COVID-19 pandemic. Digital tools, platforms, applications, and the communications among people have generated vast amounts of data ('big data') across disparate locations. Big data technologies aim at harnessing the power of extensive data in real-time or otherwise ( Daniel, 2019). The characteristic attributes of big data are often referred to as the four V's. That is, volume (amount of data), variety (diversity of sources and types of data), velocity (speed of data transmission and generation), and veracity (the accuracy and trustworthiness of data) ( Laney, 2001; Schroeck et al., 2012; Geczy, 2014). Recently, a 5th V was added, namely value (i.e., that data could be monetized; Dijcks, 2013). Because of intrinsic big data characteristics (the five Vs), large and complex datasets are impossible to process and utilize by using traditional data management techniques. Hence, novel and innovative computational technologies are required for the acquisition, storage, distribution, analysis, and management of big data ( Lazer et al., 2014; Geczy, 2015). Big data analytics commonly encompasses the processes of gathering, analyzing, and evaluating large datasets. Extraction of actionable knowledge and viable patterns from data are often viewed as the core benefits of the big data revolution ( Mayer-Schönberger and Cukier, 2013; Jagadish et al., 2014). Big data analytics employ a variety of technologies and tools, such as statistical analysis, data mining, data visualization, text analytics, social network analysis, signal processing, and machine learning ( Chen and Zhang, 2014).

As a subset of AI, machine learning focuses on building computer systems that can learn from and adapt to data automatically without explicit programming ( Jordan and Mitchell, 2015). Machine learning algorithms can provide new insights, predictions, and solutions to customize the needs and circumstances of each individual. With the availability of large quantity and high-quality input training data, machine learning processes can achieve accurate results and facilitate informed decision making ( Manyika et al., 2011; Gobert et al., 2012, 2013; Gobert and Sao Pedro, 2017). These data-intensive, machine learning methods are positioned at the intersection of big data and AI, and are capable of improving the services and productivity of education, as well as many other fields including commerce, science, and government.

Regarding education, our main area of interest here, the application of AI technologies can be traced back to approximately 50 years ago. The first Intelligent Tutoring System "SCHOLAR" was designed to support geography learning, and was capable of generating interactive responses to student statements ( Carbonell, 1970). While the amount of data was relatively small at that time, it was comparable to the amount of data collected in other traditional educational and psychological studies. Research on AI in education over the past few decades has been dedicated to advancing intelligent computing technologies such as intelligent tutoring systems ( Graesser et al., 2005; Gobert et al., 2013; Nye, 2015), robotic systems ( Toh et al., 2016; Anwar et al., 2019), and chatbots ( Smutny and Schreiberova, 2020). With the breakthroughs in information technologies in the last decade, educational psychologists have had greater access to big data. Concretely speaking, social media (e.g., Facebook, Twitter), online learning environments [e.g., Massive Open Online Courses (MOOCs)], intelligent tutoring systems (e.g., AutoTutor), learning management systems (LMSs), sensors, and mobile devices are generating ever-growing amounts of dynamic and complex data containing students' personal records, physiological data, learning logs and activities, as well as their learning performance and outcomes ( Daniel, 2015). Learning analytics, described as "the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs" ( Long and Siemens, 2011, p. 34), are often implemented to analyze these huge amounts of data ( Aldowah et al., 2019). Machine learning and AI techniques further expand the capabilities of learning analytics ( Zawacki-Richter et al., 2019). The essential information extracted from big data could be utilized to optimize learning, teaching, and administration ( Daniel, 2015). Hence, research on big data and AI is gaining increasing significance in education ( Johnson et al., 2011; Becker et al., 2017; Hwang et al., 2018) and psychology ( Harlow and Oswald, 2016; Yarkoni and Westfall, 2017; Adjerid and Kelley, 2018; Cheung and Jak, 2018). Recently, the adoption of big data and AI in the psychology of learning and teaching has been trending as a novel method in cutting-edge educational research ( Daniel, 2015; Starcic, 2019).

The Position Formulation

A growing body of literature has attempted to uncover the value of big data at different education levels, from preschool to higher education ( Chen N.-S. et al., 2020). Several journal articles and book chapters have presented retrospective descriptions and the latest advances in the rapidly expanding research area from different angles, including systematic literature review ( Zawacki-Richter et al., 2019; Quadir et al., 2020), bibliometric study ( Hinojo-Lucena et al., 2019), qualitative analysis ( Malik et al., 2019; Chen L. et al., 2020), and social network analysis ( Goksel and Bozkurt, 2019). More details can be found in the previously mentioned reviews. In this paper, we aim at presenting the current progress of the application of big data and AI in education. By and large, the research on the learner side is devoted to identifying students' learning and affective behavior patterns and profiles, improving methods of assessment and evaluation, predicting individual students' learning performance or dropouts, and providing adaptive systems for personalized support ( Papamitsiou and Economides, 2014; Zawacki-Richter et al., 2019). On the teacher side, numerous studies have attempted to enhance course planning and curriculum development, evaluation of teaching, and teaching support ( Zawacki-Richter et al., 2019; Quadir et al., 2020). Additionally, teacher dashboards, such as Inq-Blotter, driven by big data techniques are being used to inform teachers' instruction in real time while students simultaneously work in Inq-ITS ( Gobert and Sao Pedro, 2017; Mislevy et al., 2020). Big data technologies employing learning analytics and machine learning have demonstrated high predictive accuracy of students' academic performance ( Huang et al., 2020). Only a small number of studies have focused on the effectiveness of learning analytics programs and AI applications. However, recent findings have revealed encouraging results in terms of improving students' academic performance and retention, as well as supporting teachers in learning design and teaching strategy refinement ( Viberg et al., 2018; Li et al., 2019; Sonderlund et al., 2019; Mislevy et al., 2020).

Despite the growing number of reports and methods outlining implementations of big data and AI technologies in educational environments, we see a notable gap between contemporary technological capabilities and their utilization for education. The fast-growing education industry has developed numerous data processing techniques and AI applications, which may not be guided by current theoretical frameworks and research findings from psychology of learning and teaching. The rapid pace of technological progress and relatively slow educational adoption have contributed to the widening gap between technology readiness and its application in education ( Macfadyen, 2017). There is a pressing need to reduce this gap and stimulate technological adoption in education. This work presents varying viewpoints and their controversial issues, contemporary research, and prospective future developments in adoption of big data and AI in education. We advocate an interdisciplinary approach that encompasses educational, technological, and governmental spheres of influence. In the educational domain, there is a relative lack of knowledge and skills in AI and big data applications. On the technological side, few data scientists and AI developers are familiar with the advancements in education psychology, though this is changing with the advent of graduate programs at the intersection of Learning Sciences and Computer Science. Finally, in terms of government policies, the main challenges faced are the regulatory and ethical dilemmas between support of educational reforms and restrictions on adoptions of data-oriented technologies.

An Interdisciplinary Approach to Educational Adoption of Big Data and AI

In response to the new opportunities and challenges that the big data explosion and AI revolution are bringing, academics, educators, policy-makers, and professionals need to engage in productive collaboration. They must work together to cultivate our learners' necessary competencies and essential skills important for the 21st century work, driven by the knowledge economy ( Bereiter, 2002). Collaboration across diverse disciplines and sectors is a demanding task-particularly when individual sides lack a clear vision of their mutually beneficial interests and the necessary knowledge and skills to realize that vision. We highlight several overlapping spheres of interest at the intersection of research, policy-making, and industry engagements. Researchers and the industry would benefit from targeted educational technology development and its efficient transfer to commercial products. Businesses and governments would benefit from legislature that stimulates technology markets while suitably protecting data and users' privacy. Academics and policy makers would benefit from prioritizing educational reforms enabling greater adoption of technology-enhanced curricula. The recent developments and evolving future trends at intersections between researchers, policy-makers, and industry stakeholders arising from advancements and deployments of big data and AI technologies in education are illustrated in Figure 1.

Figure 1. Contemporary developments and future trends at the intersections between research, policy, and industry driven by big data and AI advances in education.

The constructive domains among stakeholders progressively evolve along with scientific and technological developments. Therefore, it is important to reflect on longer-term projections and challenges. The following sections highlight the novel challenges and future directions of big data and AI technologies at the intersection of education research, policy-making, and industry.

Big Data and AI in Education: Research

An understanding of individual differences is critical for developing pedagogical tools to target specific students and to tailor education to individual needs at different stages. Intelligent educational systems employing big data and AI techniques are capable of collecting accurate and rich personal data. Data analytics can reveal students' learning patterns and identify their specific needs ( Gobert and Sao Pedro, 2017; Mislevy et al., 2020). Hence, big data and AI have the potential to realize individualized learning to achieve precision education ( Lu et al., 2018). We see the following emerging trends, research gaps, and controversies in integrating big data and AI into education research so that there is a deep and rigorous understanding of individual differences that can be used to personalize learning in real time and at scale.

(1) Education is progressively moving from a one-size-fits-all approach to precision education or personalized learning ( Lu et al., 2018; Tsai et al., 2020). The one-size-fits-all approach was designed for average students, whereas precision education takes into consideration the individual differences of learners in their learning environments, along with their learning strategies. The main idea of precision education is analogous to "precision medicine," where researchers harvest big data to identify patterns relevant to specific patients such that prevention and treatment can be customized. Based on the analysis of student learning profiles and patterns, precision education predicts students' performance and provides timely interventions to optimize learning. The goal of precision education is to improve the diagnosis, prediction, treatment, and prevention of learning outcomes ( Lu et al., 2018). Contemporary research gaps related to adaptive tools and personalized educational experiences are impeding the transition to precision education. Adaptive educational tools and flexible learning systems are needed to accommodate individual learners' interaction, pace, and learning progress, and to fit the specific needs of the individual learners, such as students with learning disabilities ( Xie et al., 2019; Zawacki-Richter et al., 2019). Hence, as personalized learning is customized for different people, researchers are able to focus on individualized learning that is adaptive to individual needs in real time ( Gobert and Sao Pedro, 2017; Lu et al., 2018).

(2) The research focus on deploying AI in education is gradually shifting from a computational focus that demonstrates use cases of new technology to cognitive focus that incorporates cognition in its design, such as perception ( VanRullen, 2017), emotion ( Song et al., 2016), and cognitive thinking ( Bramley et al., 2017). Moreover, it is also shifting from a single domain (e.g., domain expertise, or expert systems) to a cross-disciplinary approach through collaboration ( Spikol et al., 2018; Krouska et al., 2019) and domain transfers ( L'heureux et al., 2017). These controversial shifts are facilitating transitions from the knowing of the unknown (gaining insights through reasoning) to the unknown of the unknown (figuring out hidden values and unknown results through algorithms) ( Abed Ibrahim and Fekete, 2019; Cutumisu and Guo, 2019). In other words, deterministic learning, aimed at deductive/inductive reasoning and inference engines, predominated in traditional expert systems and old AI. Whereas, today, dynamic and stochastic learning, the outcome of which involves some randomness and uncertainty, is gradually becoming the trend in modern machine learning techniques.

(3) The format of machine-generated data and the purpose of machine learning algorithms should be carefully designed. There is a notable gap between theoretical design and its applicability. A theoretical model is needed to guide the development, interpretation, and validation of algorithms ( Gobert et al., 2013; Hew et al., 2019). The outcomes of data analytics and algorithmically generated evidence must be shared with educators and applied with caution. For instance, efforts to algorithmically detect mental states such as boredom, frustration, and confusion ( Baker et al., 2010) must be supported by the operational definitions and constructs that have been prudently evaluated. Additionally, the affective data collected by AI systems should take into account the cultural differences combined with contextual factors, teachers' observations, and students' opinions ( Yadegaridehkordi et al., 2019). Data need to be informatively and qualitatively balanced, in order to avoid implicit biases that may propagate into algorithms trained on such data ( Staats, 2016).

(4) There are ethical and algorithmic challenges when balancing human provided learning and machine assisted learning. The significant influence of AI and contemporary technologies is a double-edged sword ( Khechine and Lakhal, 2018). On the one hand, it facilitates better usability and drives progress. On the other, it might lead to the algorithmic bias and loss of certain essential skills among students who are extensively relying on technology. For instance, in creativity- or experience-based learning, technology may even become an obstacle to learning, since it may hinder students from attaining first-hand experiences and participating in the learning activities ( Cuthbertson et al., 2004). Appropriately balancing the technology adoption and human involvement in various educational contexts will be a challenge in the foreseeable future. Nonetheless, the convergence of human and machine learning has the potential for highly effective teaching and learning beyond the simple "sum of the parts of human and artificial intelligence" ( Topol, 2019).

(5) Algorithmic bias is another controversial issue ( Obermeyer et al., 2019). Since modern AI algorithms extensively rely on data, their performance is governed solely by data. Algorithms adapt to inherent qualitative and quantitative characteristics of data. For example, if data is unbalanced and contains disproportionately better information on students from general population in comparison to minorities, the algorithms may produce systematic and repeatable errors disadvantaging minorities. These controversial issues need to be addressed before its wide implementation in education practice since every single student is precious. More rigorous studies and validation in real learning environments are required though work along these lines is being done ( Sao Pedro et al., 2013).

(6) The fast expansion of technology and inequalities of learning opportunities has aroused great controversies. Due to the exponential nature of technological progress, particularly big data and AI revolution, a fresh paradigm and new learning landscape are on the horizon. For instance, the elite smartphone 10 years ago, in 2010, was BlackBerry. Today, 10 years later, even in sub-Saharan Africa, 75% of the population has mobile phones several generations more advanced ( GSMA Intelligence, 2020). Hence, the entry barriers are shifting from the technical requirements to the willingness of and/or need for adoption. This has been clearly demonstrated during the COVID-19 pandemic. The need for social distancing and continuing education has led to online/e-learning deployments within months ( United Nations, 2020). A huge amount of learning data is created accordingly. The extraction of meaningful patterns and the discovery of knowledge from these data is expected to be carried out through learning analytics and AI techniques. Inevitably, the current learning cultures, learning experiences, and classroom dynamics are changing as "we live algorithmic lives" ( Bucher, 2018). Thus, there is a critical need to adopt proper learning theories of educational psychology and to encourage our learners to be active participants rather than passive recipients or merely tracked objects ( Loftus and Madden, 2020). For example, under the constructionist framework ( Tsai, 2000), the technology-enhanced or AI-powered education may empower students to know their learning activities and patterns, predict their possible learning outcomes, and strategically regulate their learning behavior ( Koh et al., 2014; Loftus and Madden, 2020). On the other hand, in the era of information explosion and AI revolution, the disadvantaged students and developing countries are indeed facing a wider digital divide. To reduce the inequalities and bring more opportunities, cultivating young people's competencies is seemed like one of the most promising means ( UNESCO, 2015). Meanwhile, overseas support from international organizations such as World Bank and UNESCO are imperative for developing countries in their communication infrastructure establishment (e.g., hardware, software, connectivity, electricity). Naturally, technology will not replace or hinder human learning; rather, a smart use of new technologies will facilitate transfer and acquisition of knowledge ( Azevedo et al., 2019).

An overarching theme from the above trends of research is that we need theories of cognitive and educational psychology to guide our understanding of the individual learner (and individual differences), in order to develop best tools, algorithms, and practices for personalized learning. Take, for example, VR (virtual reality) or AR (augmented reality) as a fast-developing technology for education. The industry has developed many different types of VR/AR applications (e.g., Google Expeditions with over 100 virtual field trips), but these have typically been developed in the views of the industry (see further discussion below) and may not be informed by theories and data from educational psychology about how students actually learn. To make VR/AR effective learning tools, we must separate the technological features from the human experiences and abilities (e.g., cognitive, linguistic, spatial abilities of the learner; see Li et al., 2020). For example, VR provides a high-fidelity 3D real-life virtual environment, and the technological tools are built on the assumption that 3D realism enables the learner to gain 'perceptual grounding' during learning (e.g., having access to visual, auditory, tactile experiences as in real world). Following the 'embodied cognition' theory ( Barsalou, 2008), we should expect VR learning to yield better learning outcomes compared with traditional classroom learning. However, empirical data suggest that there are significant individual differences in that some students benefit more than others from VR learning. It may be that the individuals with higher cognitive and perceptual abilities need no additional visuospatial information (provided in VR) to succeed in learning. In any case, we need to understand how embodied experiences (provided by the technology) interact with different learners' inherent abilities (as well as their prior knowledge and background) for the best application of the relevant technology in education.

Big Data and AI in Education: Policy-Making

Following the revolution triggered by breakthroughs in big data and AI technology, policy-makers have attempted to formulate strategies and policies regarding how to incorporate AI and emerging technologies into primary, secondary, and tertiary education ( Pedró et al., 2019). Major challenges must be overcome in order to suitably integrate big data and AI into educational practice. The following three segments highlight pertinent policy-oriented challenges, gaps, and evolving trends.

(1) In digitally-driven knowledge economies, traditional formal education systems are undergoing drastic changes or even a paradigm shift ( Peters, 2018). Lifelong learning is quickly being adopted and implemented through online or project-based learning schemes that incorporate multiple ways of teaching ( Lenschow, 1998; Sharples, 2000; Field, 2001; Koper and Tattersall, 2004). This new concept of continual education will require micro-credits or micro-degrees to sustain learners' efforts ( Manuel Moreno-Marcos et al., 2019). The need to change the scope and role of education will become evident in the near future ( Williams, 2019). For example, in the next few years, new instruction methods, engagement, and assessment will need to be developed in formal education to support lifelong education. The system should be based on micro-credits or micro-degrees.

(2) Solutions for integrating cutting-edge research findings, innovative theory-driven curricula, and emerging technologies into students' learning are evidently beneficial, and perhaps even ready for adoption. However, there is an apparent divergence between a large number of pre-service and in-service teachers and their willingness to support and adopt these emerging technologies ( Pedró et al., 2019). Pre-service teachers have greater exposure to modern technologies and, in general, are more willing to adopt them. In-service teachers have greater practical experience and tend to more rely on it. To bridge the gap, effective teacher education programs and continuing education programs have to be developed and offered to support the adoption of these new technologies so that they can be implemented with fidelity ( O'Donnell, 2008). This issue could become even more pressing to tackle in light of the extended period of the COVID-19 pandemic.

(3) A suitable legislative framework is needed to protect personal data from unscrupulous collection, unauthorized disclosure, commercial exploitation, and other abuses ( Boyd and Crawford, 2012; Pardo and Siemens, 2014). Education records and personal data are highly sensitive. There are significant risks associated with students' educational profiles, records, and other personal data. Appropriate security measures must be adopted by educational institutions. Commercial educational system providers are actively exploiting both legislative gaps and concealed data acquisition channels. Increasing numbers of industry players are implementing data-oriented business models ( Geczy, 2018). There is a vital role to play for legislative, regulatory, and enforcing bodies at both the national and local levels. It is pertinent that governments enact, implement, and enforce privacy and personal data protection legislation and measures. In doing so, there is a need to strike a proper balance between desirable use of personal data for educational purposes and undesirable commercial monetization and abuse of personal data.

Big Data and AI in Education: Industry

As scientific and academic aspects of big data and AI in education have their unique challenges, so does the commercialization of educational tools and systems ( Renz et al., 2020). Numerous countries have attempted to stimulate innovation-based growth through enhancing technology transfer and fostering academia-industry collaboration ( Huggins and Thompson, 2015). In the United States, this was initiated by the Bayh-Dole Act ( Mowery et al., 2001). Building a reciprocal and sustained partnership is strongly encouraged. It facilitates technology transfers and strengthens the links between academia and the education industry. There are several points to be considered when approaching academia-industry collaboration. It is important that collaboration is mutually beneficial. The following points highlight the overlapping spheres of benefits for both educational commerce and academia. They also expose existing gaps and future prospects.

(1) Commercializing intelligent educational tools and systems that include the latest scientific and technological advances can provide educators with tools for developing more effective curricula, pedagogical frameworks, assessments, and programs. Timely release of educational research advances onto commercial platforms is desirable by vendors from development, marketing, and revenue perspectives ( Renz and Hilbig, 2020). Implementation of the latest research enables progressive development of commercial products and distinctive differentiation for marketing purposes. This could also potentially solve the significant gap between what the industry knows and develops and what the academic research says with regard to student learning. Novel features may also be suitably monetized-hence, expanding revenue streams. The gaps between availability of the latest research and its practical adoption are slowing progress and negatively impacting commercial vendors. A viable solution is a closer alignment and/or direct collaboration between academia and industry.

(2) A greater spectrum of commercially and freely available tools helps maintain healthy market competition. It also helps to avoid monopolies and oligopolies that stifle innovation, limit choices, and damage markets for educational tools. Some well-stablished or free-of-charge platforms (e.g., Moodle, LMS) might show such potential of oligopolies during the COVID-19 pandemic. With more tools available on the market, educators and academics may explore novel avenues for improving education and research. New and more effective forms of education may be devised. For instance, multimodal virtual educational environments have high potential future prospects. These are environments that would otherwise be impossible in conventional physical settings (see previous discussion of VR/AR). Expanding educational markets and commerce should inevitably lead to expanding resources for research and development funding ( Popenici and Kerr, 2017). Collaborative research projects sponsored by the industry should provide support and opportunities for academics to advance educational research. Controversially, in numerous geographies there is a decreasing trend in collaborative research. To reverse the trend, it is desirable that academic researchers and industry practitioners increase their engagements via mutual presentations, educations, and even government initiatives. All three stakeholders (i.e., academia, industry, and government) should play more active roles.

(3) Vocational and practical education provides numerous opportunities for fruitful academia-industry collaboration. With the changing nature of work and growing technology adoption, there is an increasing demand for radical changes in vocational education-for both teachers and students ( World Development and Report, 2019). Domain knowledge provided by teachers is beneficially supplemented by AI-assisted learning environments in academia. Practical skills are enhanced in industrial environments with hands-on experience and feedback from both trainers and technology tools. Hence, students benefit from acquiring domain knowledge and enhancing their skills via interactions with human teachers and trainers. Equally, they benefit from gaining the practical skills via interactions with simulated and real-world technological environments. Effective vocational training demands teachers and trainers on the human-learning side, and AI environments and actual technology tools on machine-learning side. Collaboration between academia and industry, as well as balanced human and machine learning approaches are pertinent for vocational education.

Discussion and Conclusion

Big data and AI have enormous potential to realize highly effective learning and teaching. They stimulate new research questions and designs, exploit innovative technologies and tools in data collection and analysis, and ultimately become a mainstream research paradigm ( Daniel, 2019). Nonetheless, they are still fairly novel and unfamiliar to many researchers and educators. In this paper, we have described the general background, core concepts, and recent progress of this rapidly growing domain. Along with the arising opportunities, we have highlighted the crucial challenges and emerging trends of big data and AI in education, which are reflected in educational research, policy-making, and industry. Table 1 concisely summarizes the major challenges and possible solutions of big data and AI in education. In summary, future studies should be aimed at theory-based precision education, incorporating cross-disciplinary application, and appropriately using educational technologies. The government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration.

Table 1. Major challenges and possible solutions for integrating big data and AI into education.

Regarding the future development of big data and AI, we advocate an in-depth dialog between the supporters of "cold" technology and "warm" humanity so that users of technology can benefit from its capacity and not see it as a threat to their livelihood. An equally important issue is that overreliance on technology may lead to an underestimation of the role of humans in education. Remember the fundamental role of schooling: the school is a great equalizer as well as a central socialization agent. We need to better understand the role of social and affective processing (e.g., emotion, motivation) in addition to cognitive processing in student learning successes (or failures). After all, human learning is a social behavior, and a number of key regions in our brains are wired to be socially engaged (see Li and Jeong, 2020 for a discussion).

It has been estimated that approximately half of the current routine jobs might be automated in the near future ( Frey and Osborne, 2017; World Development and Report, 2019). However, the teacher's job could not be replaced. The teacher-student relationship is indispensable in students' learning, and inspirational in students' personal growth ( Roorda et al., 2011; Cheng and Tsai, 2019). On the other hand, new developments in technologies will enable us to collect and analyze large-scale, multimodal, and continuous real-time data. Such data-intensive and technology-driven analysis of human behavior, in real-world and simulated environments, may assist teachers in identifying students' learning trajectories and patterns, developing corresponding lesson plans, and adopting effective teaching strategies ( Klašnja-Milicevic et al., 2017; Gierl and Lai, 2018). It may also support teachers in tackling students' more complex problems and cultivating students' higher-order thinking skills by freeing the teachers from their monotonous and routine tasks ( Li, 2007; Belpaeme et al., 2018). Hence, it is now imperative for us to embrace AI and technology and prepare our teachers and students for the future of AI-enhanced and technology-supported education.

The adoption of big data and AI in learning and teaching is still in its infancy and limited by technological and mindset challenges for now; however, the convergence of developments in psychology, data science, and computer science shows great promise in revolutionizing educational research, practice, and industry. We hope that the latest achievements and future directions presented in this paper will advance our shared goal of helping learners and teachers pursue sustainable development.

Author Contributions

HLu wrote the initial draft of the manuscript. PG, HLa, JG, and PL revised the drafts and provided theoretical background. SY, HO, JB, and RG contributed content for the original draft preparation of the manuscript. C-CT provided theoretical focus, design, draft feedback, and supervised throughout the research. All authors contributed to the article and approved the submitted version.

Funding

This work was financially supported by the Institute for Research Excellence in Learning Sciences of National Taiwan Normal University (NTNU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

Conflict of Interest

JG was employed by company Apprendis, LLC, Berlin.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Challenges and Future Directions of Big Data and Artificial Intelligence in Education

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We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research.

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Education and Information Technologies 28, 4919-4939 (2023) Cite this article

Abstract

Artificial Intelligence (AI) has become an important technology affecting the development of society and education, and it is crucial to explore AI to enhance students' creativity and learning performance. This research proposes the model and hypothesis based on the resource-based theory and related research. AI of higher education institute (HEI) affects students' learning performance and combines the existing literature to develop measurement tools and to obtain a formal questionnaire after pre-research and received 561 valid questionnaires collected from HEIs in China that have applied AI. Then we used SmartPLS 3.0 to construct a partial least squares structural equation model (PLS-SEM) for data analysis on the received data samples. The research results show that: 1) HEIs' artificial intelligence capability is a three-order variable and formed by three formative second-order variables such as resources (data, technical, basic resources), skills (technical skills, teaching applications, collaboration competencies), and consciousness (reform, innovation consciousness); 2) HEIs' artificial intelligence capability significantly affects students' self-efficacy and creativity; 3) HEIs' artificial intelligence capability affects students' learning performance via two mediating variables: student creativity and self-efficacy. This study focuses on AI applications within the HEI, confirms the new explanatory power of resource-based theory in technological practices, and deconstructs the intrinsic mechanics, especially in relationships between students' creativity, self-efficacy, and learning performance. This research also puts forward suggestions to reserve and deploy artificial intelligence resources, improve the digital literacy of teachers and students, use AI to drive teaching and learning, and improve students' creativity and learning performance.

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The datasets used or analysed during the current study are available from the author on reasonable request.

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Acknowledgements

Thanks to Mengti Li for her assistance in the language expression of the article.

Funding

This work was supported by the Ningbo Philosophy and Social Science Planning Project [G22-5-JY09]; Ningbo Education Science Planning Project under Grant [2022YZD012]; Ningbo Soft Science Research Program under Grant [2022R040]; Zhejiang Federation of Humanities and Social Sciences Circles under Grant [2023N073]; and Zhejiang Province Association for Higher Education Project under Grant [KT2022412].

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Authors and Affiliations

1. School of Logistics and E-Commerce, Zhejiang Wanli University, Ningbo, 315000, China

Shaofeng Wang

2. Smart Learning Institute, Beijing Normal University, Beijing, 100875, China

Shaofeng Wang, Zhuo Sun & Ying Chen

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Conceptualization, S.W. and Y.C.; methodology, S.W.; software, S.W.; validation, S.W., Z.S. and Y.C.; formal analysis, S.W.; investigation, S.W.; resources, S.W.; data curation, S.W.; writing -original draft preparation, S.W. and Y.C.; writing-review and editing, Z.S.; visualization, S.W.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhuo Sun.

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Wang, S., Sun, Z. & Chen, Y. Effects of higher education institutes' artificial intelligence capability on students' self-efficacy, creativity and learning performance. Educ Inf Technol 28, 4919-4939 (2023). https://doi.org/10.1007/s10639-022-11338-4
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Keywords

Artificial intelligence capability

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Learning performance

Student creativity

Self-efficacy

PLS-SEM

Higher-order Constructs

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# data[].attributes.attachment_link.title
Effects of higher education institutes' artificial intelligence capability on students' self-efficacy, creativity and learning performance - Education and Information Technologies

# data[].attributes.attachment_link.description
Artificial Intelligence (AI) has become an important technology affecting the development of society and education, and it is crucial to explore AI to enhance students' creativity and learning performance. This research proposes the model and hypothesis based on the resource-based theory and related research.

# data[].attributes.attachment_link.readable_text
Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey

Abstract

Objectives

To explore the attitudes of United Kingdom (UK) medical students regarding artificial intelligence (AI), their understanding, and career intention towards radiology. We also examine the state of education relating to AI amongst this cohort.

Methods

UK medical students were invited to complete an anonymous electronic survey consisting of Likert and dichotomous questions.

Results

Four hundred eighty-four responses were received from 19 UK medical schools. Eighty-eight percent of students believed that AI will play an important role in healthcare, and 49% reported they were less likely to consider a career in radiology due to AI. Eighty-nine percent of students believed that teaching in AI would be beneficial for their careers, and 78% agreed that students should receive training in AI as part of their medical degree. Only 45 students received any teaching on AI; none of the students received such teaching as part of their compulsory curriculum. Statistically, students that did receive teaching in AI were more likely to consider radiology ( p = 0.01) and rated more positively to the questions relating to the perceived competence in the post-graduation use of AI ( p  = 0.01-0.04); despite this, a large proportion of students in the taught group reported a lack of confidence and understanding required for the critical use of healthcare AI tools.

Conclusions

UK medical students understand the importance of AI and are keen to engage. Medical school training on AI should be expanded and improved. Realistic use cases and limitations of AI must be presented to students so they will not feel discouraged from pursuing radiology.

Key points

UK medical students do not feel adequately prepared to work alongside AI, but understand the increasing importance of AI in healthcare and would like to receive teaching on the subject

A significant number of UK medical students are discounting radiology as a possible career due to AI. Students that received AI teaching were less likely to rule out a career in radiology

Although the small number of students that received AI teaching felt more confident in working with AI in the future compared to students that did not receive teaching, a significant number of taught students still felt inadequately prepared.

Introduction

The application of artificial intelligence (AI) in healthcare continues to generate increasing interest. Within radiology, there are now AI tools that employ deep learning methods such as convolutional neural networks (CNNs) which are effective at performing narrow classification tasks where large training datasets are available. High profile examples include the classification of chest radiographs based on abnormality for triage [ 1] or pathological processes [ 2, 3]. Multiple studies have demonstrated the potential utility of such AI algorithms in other medical specialties including ophthalmology [ 4], dermatology [ 5], and pathology [ 6]. Although there is currently a paucity of evidence to support routine use of AI algorithms in real-world clinical practice, it is expected that with increasing academic and industry interest, validated use cases for AI tools in radiology will likely emerge rapidly [ 7].

As the field of healthcare AI continues to expand, it is increasingly apparent that AI education for clinicians and medical students is needed. In the United Kingdom (UK), the use of novel digital methods in health has been the subject of a recent national governmental review [ 8]. Of note, the Topol review places explicit emphasis on the importance of arming the current and future clinical workforce with the necessary skills to work critically with novel digital tools, including those underpinned by AI. Currently, UK medical schools are not mandated to include this in the curriculum by the General Medical Council. There exists some anecdotal evidence that certain UK medical schools do provide limited educational opportunities in healthcare AI; however, the actual prevalence of such training and the level of AI literacy amongst the UK medical student population remains unknown. Furthermore, to effectively engage medical students on this topic, it would be useful to understand their perception towards AI as a cohort. It is not unreasonable to assume the current environment of enthusiasm towards AI exerts some influence on medical student attitude and behaviour. Of specific relevance to radiology, this has been previously studied by groups in Canada [ 9] and Germany [ 10], where multi-centre surveys were conducted on medical students from their respective countries. The Canadian group found that a significant proportion of Canadian medical students were less likely to consider radiology as a career due to fear of replacement. Although the German group reported that only a minority of students believed that AI would replace radiologists, actual career intentions were not examined. We are unaware of any published studies investigating the views and attitudes that UK medical students hold towards AI, nor are we aware of any European study that examines the influence exerted upon medical students by the development of AI.

In this study, we aim to understand UK medical students' views on AI and explore if AI influences their career intentions with specific regard to radiology. We also collated information on medical students' understanding of AI and assessed their level of confidence in working alongside AI in the future.

Materials and methods

Students from UK medical schools were invited to complete an electronic survey (Google Forms, Google LLC) designed jointly by clinicians with an interest in AI and/or medical education. Contact was made with all UK medical schools with a request to circulate the survey to all currently enrolled students. Students were also directly invited to complete the survey via social media. Students were required to enter their institutional email address at the start of the survey, and only individuals with a valid UK medical school email address were able to participate; all responses were subsequently anonymised. It was made explicitly clear to participants at the start of the survey that their responses were anonymous. Minimal risk ethical approval was obtained from the King's College London research ethics office (KCL-MRA18/19-11127). Informed consent was confirmed with individual participants at the start of the survey.

The survey design underwent several rounds of iteration, and final validation was performed with a group of 30 medical students from King's College London who were not included in the final survey. The final survey consisted of 11 5-point Likert questions, whereby participants rated their agreement towards a presented statement relating to their current attitudes towards AI, their career intentions towards radiology, their current understanding of AI, and their confidence in using AI tools in a routine and critical manner following graduation. Dichotomous questioning was used to determine if participants received teaching on AI and if this teaching formed a compulsory part of their curriculum. The questionnaire is attached as Additional file 1.

Statistical analysis was performed using R (ver. 3.5.3; Great Truth) [ 11] with RStudio (ver. 1.2.1335) [ 12]. Simple descriptive statistics are presented in percentages. An unpaired two-tailed Wilcoxon rank-sum test was performed to compare the responses relating to career choice and perceived competence in post-qualification use of AI tools between the group of students which received teaching in AI versus the group of students that did not. A p value of less than 0.05 was considered statistically significant.

Results

A total of 484 responses were received from 19 out of 34 UK medical schools currently awarding medical degrees recognised by the General Medical Council. The majority (88%, n = 432) of respondents believe that AI will play an important role in healthcare in the future, rating their responses as either strongly agree (44.2% n = 214) or agree (45% n = 218). Just under half of the respondents (strongly agree or agree, 49.2% n = 238) reported they were less likely to consider a career in radiology due to AI, compared to those who do not feel they are less likely to consider a career in radiology due to AI (strongly disagree or disagree, 27.1% n = 131). A similar number of students also believed that some specialities will be replaced by AI within their lifetime (strongly agree/agree, 48.3% n = 234; strongly disagree/disagree, 36.8% n = 178) (Fig. 1).

With regard to the questions relating to the current understanding of AI, nearly half the respondents selected responses indicating they have an understanding of the basic computational principles that underpin AI; 44.6% ( n = 214) of respondents selecting strongly agree or agree, whilst 43.4% ( n = 210) selected disagree or strongly disagree, and the remaining 12.4% ( n = 60) were neutral. With regard to the current limitations of AI, more students reported they had an understanding on this than those who did not; 48.3% selected strongly agree or agree to this question (7.2% n = 35 and 41.1% n = 199, respectively), whilst 30.4% selecting strongly disagree or disagree (5.2% n = 25 and 25.2% n = 122, respectively), and 21.3% ( n = 103) selected neutral. More students reported that they did not feel comfortable with the nomenclature associated with AI (disagree and strongly disagree, 43.4% n = 210), than those that reported otherwise (agree and strongly agree, 30.2% n = 146) (Fig. 2).

An overwhelming majority of students believed that teaching in AI would be beneficial for their careers with 88.8% students responding either strongly agree (35.1% n = 170) or agree (53.7% n = 260); 9.7% ( n = 47) of students submitted neutral responses on this question. 1.2% of students ( n = 6) disagreed with this statement. Along this line of questioning, a significant majority of students (78.1%) also agreed with the statement that all medical students should receive training in AI as part of their medical degree; 28.1% ( n = 136) and 50% ( n = 242) of students selected strongly agree and agree, respectively. Neutral responses were recorded by 16.1% ( n = 78) of students, and only 5.6% ( n = 27) of students responded negatively when asked if all students should receive training in AI (Fig. 3).

Out of a total of 484 students, only 45 (9.2% of students surveyed) received some form of teaching on AI. Students from 12 out of the 19 medical schools reported teaching opportunities relating to AI, but none of these students received such teaching as a mandatory part of the curriculum. 86.7% ( n = 39) of students found that teaching in AI was useful. Students who received no teaching were less likely to consider radiology as a possible career choice in the future compared to students who received teaching in AI (Wilcox test, p = 0.01) (Fig. 4).

The majority of students reported they would not feel ready to work with AI by the end of their degree, only 10.4% ( n = 50) of students agreed that they would be confident in using AI tools if required, and the same number of students agreed that they would possess a basic understanding in the methods used to assess AI performance. A slightly larger number of students felt they would possess the knowledge needed to work with AI in routine clinical practice (11.3% n = 55). Students who receive training in AI recorded more positive responses to these questions, rating themselves as being more confident in the use of basic AI clinical tools if required ( p = 0.04), possess a better understanding in the assessment of AI algorithm performance ( p = < 0.01), and overall felt more likely to have the knowledge required to work with AI in routine clinical practice ( p = < 0.01) (Fig. 5).

Discussion

It is now accepted that AI will likely have a profound impact in the future practice of radiology and other medical fields. This sentiment is shared by a majority of students in our cohort with 88% agreeing that AI will play an important role in healthcare. Despite convincing arguments that AI will not replace radiologists [ 13], almost half of the students in our study are less likely to consider a career in radiology due to the perceived success of AI. In our cohort, 48.3% of students reported that they believed certain specialties would be replaced by AI. Adverse impact on radiology recruitment secondary to AI advancement was previously demonstrated by Gong et al. in a Canadian cohort ( N = 322) [ 9], where one sixth of medical students interested in radiology were discouraged from applying for a residency post. The Canadian study identified that medical students were more concerned about the displacement, rather than replacement, of radiologists which could lead to reduced workforce demands. Interestingly, a separate study from Germany by Pinto dos Santos et al (2019) ( N = 263) [ 10] showed that their cohort of students did not believe that AI would replace radiologists. Although they did not examine the career intentions of the students they surveyed, it can be indirectly inferred that German students may be less likely to rule out radiology due to AI. Our results provide evidence that the advancement of AI is having a greater detrimental effect on UK medical students considering radiology as a possible career when compared to their Canadian and German counterparts. A limitation of our study is that we only sampled a fraction of all UK medical students, and not all medical schools are represented in our cohort.

It is worrying that a large number of students in our cohort are discounting radiology as a possible career given the acute shortage of radiologists in the UK [ 14]. The work force consequences are potentially catastrophic, especially with an ever-increasing demand for imaging investigations [ 14]. We believe that student perceptions on this are heavily influenced by the narrative favoured by certain ardent proponents of AI, aided by popular media. Prominent figures from the computer sciences community and venture capital have frequently billed AI as a likely 'disruptor' of radiologists, perpetuating the fallacy that radiologists will no longer be needed in the foreseeable future. It is only more recently that an increasingly circumspect view of AI adaptation in medical imaging has gained acceptance [ 13], including by some of the same thought leaders that originally predicted the rapid demise of radiologists [ 15]. In our opinion, the persisting misconception of AI rendering radiologists obsolete should be urgently addressed. Realistic potential use cases and limitations of this technology must be presented to medical students so that they will not feel discouraged to embrace radiology as a career.

Pinto dos Santos et al (2019) [ 10] identified an overall low level of information of medical students about AI, with students stating that they acquired this from mainstream media rather than university teaching. They also highlighted that students who were more knowledgeable about AI were less afraid of working with technology. Hence, in our survey, we aimed to understand the level of AI understanding and the state of relevant education within our medical students. We considered three areas of knowledge essential in understanding the fundamentals of AI: basic understanding of the principles of AI, familiarity with associated nomenclature, and basic understanding of the current limitations of AI. Under half of the students gave responses that suggested a degree of understanding in these areas, which could explain the uncertainty of working with AI. Out of the 484 medical students surveyed, only 45 received some form of teaching in AI. None of these students received this education as part of their compulsory curriculum. It is unclear if such teaching occurred as part of their intercalated degrees, student selected modules, or self-directed learning using available online resources. It is interesting to note that the large majority of medical students in our cohort agreed that AI should be incorporated into the medical school curriculum, with a similar majority believing that an understanding on this topic would be beneficial for their careers. Our findings suggest that there is a paucity of relevant teaching amongst UK medical schools, despite the current generation of UK medical students being motivated to engage with AI and informatics teaching.

Students who did receive AI training were significantly less likely to rule out radiology as a career choice, and this may be for two reasons. Firstly, students already interested in radiology as a future career are more likely to seek information on AI to provide an informed decision on their career choice. Secondly, students who do have a better understanding of AI are likely more aware of the limitations that preclude replacement of radiologists. Furthermore, it is not unreasonable to assume that some students are encouraged by the potential opportunities that AI would present in a radiological career. Our study found that students who received teaching on the topic felt more prepared in being able to work with AI tools compared to those who did not. However, despite statistical significance between the two groups on the questions relating to post-graduation readiness on working with AI, the majority of students in the taught group still reported that they would lack both the confidence and knowledge to employ AI in a critical manner. Although most students in the taught group felt that the teaching they received is useful, our results suggest the sporadic teaching that exist within UK medical schools do not adequately prepare medical students for the impending digital revolution of healthcare. It is now accepted that AI will likely have a profound impact in the future practice of radiology and other medical fields, and it is thus inevitable that AI and other digital tools will be incorporated into clinical practice, regardless of speciality. It would be a failure if we do not seize on this opportunity to better equip our future physicians with the adequate knowledge. It is our belief that the physicians of tomorrow must possess the means to employ digital tools, including AI, in a manner that is akin to rational evidence-based medication use.

Availability of data and materials

The survey was submitted as a supplement to the manuscript.

Requests for our data can be made via the corresponding author.

Abbreviations

AI:

Artificial intelligence

CNN:

Convolutional neural networks

UK:

United Kingdom

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Funding

This study received no funding.

Author information

Author notes

Leo Monzon and Daniel Stephen Poon contributed equally to this work.

Authors and Affiliations

1. Department of Radiology, Guy's and St. Thomas' NHS Foundation Trust, London, UK

Cherry Sit, Rohit Srinivasan, Ashik Amlani, Keerthini Muthuswamy, Aishah Azam & Daniel Stephen Poon

2. Department of Interventional Radiology, Guy's and St. Thomas' NHS Foundation Trust, London, UK

Leo Monzon

3. School of Medicine, Faculty of Medicine and Life Sciences, King's College London, London, UK

Leo Monzon & Daniel Stephen Poon

Contributions

CS, RS, AA, KM, AZ, LM, and DP contributed to the methodology development. CS, RS, AA, KM, AZ, and DP contributed to the data collection. CS, AA, LM, and DP contributed to the statistical analysis of the results. CS, RS, LM, and DP contributed to the drafting of the manuscript. LM and DP contributed equally to the supervision of the project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Daniel Stephen Poon.

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Ethics approval and consent to participate

Minimal risk registration was granted by the King's College London research ethics office (MRA-18/19-11127).

Consent from individual respondents was obtained prior to their completion of the survey.

Consent for publication

The authors give consent for publication.

Competing interests

The authors declare that they have no competing interests.

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Questionnaire on current attitudes and understanding on artificial intelligence of medical students in the United Kingdom.

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Sit, C., Srinivasan, R., Amlani, A. et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging 11, 14 (2020). https://doi.org/10.1186/s13244-019-0830-7
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Keywords

Artificial intelligence

Education

Medical student

Radiology

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Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey - Insights into Imaging

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Objectives To explore the attitudes of United Kingdom (UK) medical students regarding artificial intelligence (AI), their understanding, and career intention towards radiology. We also examine the state of education relating to AI amongst this cohort. Methods UK medical students were invited to complete an anonymous electronic survey consisting of Likert and dichotomous questions.

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Employing Adaptive Learning and Intelligent Tutoring Robots for Virtual Classrooms and Smart Campuses: Reforming Education in the Age of Artificial Intelligence

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As a consequence of the advent of new technologies, teaching and learning methods have evolved dramatically. Artificial intelligence (AI) applications in educational settings are becoming increasingly apparent as a result of rapid development of AI technology in…

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The use of artificial intelligence (AI) for medicine has recently drawn much attention due to the advances in machine learning techniques involving multiple layers of artificial neural networks trained on big data, i.e., deep learning [ 1, 2]. AI is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare [ 1]. On the downside, AI has been creating much hype, too. It is not difficult to find on the Internet stories about how fast and accurately modern AI software programs can analyze the patient's medical information and automatically present diagnoses, even more precisely than human experts, with a nuance that AI will soon dominate the medical practice. However, these stories are mostly quite exaggerated or, at best, explain the matter only superficially. In reality, few AI techniques are currently used in medical practice.

Nevertheless, at the time when the current medical students will commence their career as medical professionals after completion of studies and training, various AI software tools will likely be used in clinical practice. In applying AI technology to patients, medical professionals are not ones who are in the backseat but should be in the driver's seat. They should be able to resolve any anxiety, confusion, and questions that patients and the public may have about applying AI to medicine. Medical professionals are also responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. The purpose of this article is to provide a succinct summary of the current state of AI from a medical viewpoint and suggest what medical students should do to prepare for the era of AI in medicine.

Since the Go champion Sedol Lee was defeated in early 2016 by Google's AlphaGo developed using deep learning technologies, AI has often been referred to by lay media and some people as a technology that would replace many physicians in the foreseeable future. However, 3 years later, now ironically, worries exist that many companies developing AI software tools for medical use are facing the risk of shutting down. In other words, AI software tools for medical applications that have been developed so far are not being consumed in real-world clinical practice. Why?

The most important criterion for adopting AI technology in medical practice is that the technology should help provide better quality care for patients and improve healthcare outcomes of the patients, that is, create quality and value for patients. One notable example of failure by not fulfilling this requirement is IBM's Watson for Oncology. This AI software designed to provide information to assist cancer diagnosis was released in 2013 by IBM and was introduced by 8 hospitals in Korea (Republic of) in 2016 and 2017 (none since 2018) [ 4]. However, unlike what has been exposed to the public, diagnostic suggestions provided by Watson for Oncology were not as accurate as expected. As a result, the initial atmosphere of triumph was soon superseded by a sober evaluation of advantages and drawbacks [ 5, 6]. In fact, the MD Anderson Cancer Center, one of the best cancer hospitals in the United States, attempted to introduce Watson for Oncology early in 2017, but found problems and stopped the project after having spent $62 million [ 7]. Also, IBM laid off approximately up to 70% of staff in the corresponding business division in the first half of 2018 [ 8]. Just as drugs and any other medical devices are required to pass a strict validation of safety and efficacy before they can be used for patients, thorough clinical validation before clinical adoption is critical for AI technology, too, regarding how accurate it is and how large of a benefit with patient care it can provide without creating any inadvertent harms [ 9- 13].

One of the key points to consider when evaluating the accuracy of an AI algorithm is external validation, i.e., testing the algorithm accuracy using datasets collected independently from the training dataset [ 11, 14- 17]. This is due to strong data dependency of AI algorithms. The process of training AI algorithms is different from that of human learning, as the latter is based on understanding concepts and principles, while the former is based on the search for patterns in given data without an understanding of concepts and principles [ 18]. When a large amount of data is input to a computer, it creates mathematical formulae (i.e., a mathematical representation of the patterns) that associate the data to answers to afford AI. Modern AI technologies such as deep learning are known to have high accuracy compared to past technologies in finding the patterns. However, they have a strong dependency on training data. While high accuracy is generally guaranteed within the data used for training, the accuracy for data that were not used for training can be low. The accuracy of AI algorithms cannot go beyond the information inherent to the datasets on which they are trained and cannot avoid the biases and errors in the training data. This strong data dependency of AI poses a particular concern in the medical field [ 19]. The datasets used to train AI algorithms for medical applications are prone to various selection biases and may not adequately represent target populations in real-world clinical practice for many reasons [ 19]. Also, unexpected situations can occur in real-world clinical practice at any time, not infrequently [ 19]. As a result, there is a genuine risk that the accuracy of an AI algorithm may drop if the AI software is applied to the data and patients of another hospital or data acquired from other imaging systems or methods [ 13, 20- 23]. Likewise, it is uncertain how accurately an AI algorithm would perform in various real-world practice settings until it is validated directly in such clinical environments.

For proper validation of the clinical accuracy of AI algorithms, the test data should have the following features [ 11, 16, 17]: (1) data collected avoiding biases from specific indications (i.e., target patient groups in well-defined clinical scenarios) to which the AI software will be applied; (2) data from hospitals other than the institution in which the data for AI training were collected, and (3) data collected from multiple institutions. Also, whenever possible, prospectively collected data should be used [ 11, 14, 16, 17]. However, most AI software applications for medical use developed until now have not been validated in this way [ 24], and the lack of appropriate clinical validation for AI algorithms, a phenomenon referred to as 'digital exceptionalism,' raises a significant concern [ 25, 26].

The second criterion for adopting AI technology in medicine is that it should support healthcare providers or hospital administration if not directly helps patients. One successful example well addressing this point is an AI software developed and introduced by the University of Pittsburgh Medical Center (UPMC) [ 27]. This software analyzes data of hospitalized patients and estimates the probability of re-hospitalization within seven and 30 days after discharge. In the United States, the hospital may not receive reimbursement from insurers if a patient is re-hospitalized within a short period after discharge, and the early re-hospitalizations may ultimately work as a disadvantage to the hospital when it makes a contract with insurers. Therefore, it is crucial for hospitals to reduce the rate of early re-hospitalizations. UPMC is now reported to be reaping the benefits of its investment of $100 million in the enterprise analytics [ 27].

If properly designed and used, AI technology could reinforce many weaknesses in current medical practice [ 1]. If time-consuming processes that require simple repetitive work are taken care of by AI, it would substantially reduce the fatigue of healthcare providers, and physicians could spend more time in facing with patients and concentrating on more complicated medical tasks [ 1]. AI technology may also reduce the number of inadvertent errors in clinical practice and may decrease differences in judgments among medical professionals. If patient conditions can be monitored 24 hours a day by AI systems, which would practically be impossible for humans to do, the patients may be managed more safely. Furthermore, new patterns discovered by AI through the analysis of big data from clinical practice may lead to the development of new biomarkers for diagnosis and treatment. Inputs by medical professionals who understand medicine, specific details of clinical practice, as well as patients, are critical for realizing these expectations. Many medical AI software applications developed to date were created mainly to make use of large data that happen to have already been available rather than addressing the actual needs identified by practitioners in real clinical world (i.e., definition of problems or pertinent use cases in real-world practice first, followed by data collection to address the needs). It is another reason why AI software applications developed so far are rarely used in clinical practice and highlights the importance of input by medical professionals who actually take care of patients on the spot.

It was spoken merely a few years ago that AI might soon replace specialists in radiology or pathology departments. However, with increasing understanding of AI, we now know that these premature "predictions" are only revealing a very shallow understanding of the technology and its application in medicine at the time. Do we not have to worry about AI replacing physicians then? In many cases, AI tools for medicine mostly play the role of a virtual assistant for physicians and healthcare systems, helping them to provide more accurate and efficient patient care, of which typical examples are many AI tools that have been being developed in the fields of radiology and pathology. On the other hand, in the setting of managing common chronic illnesses or primary healthcare, AI could be designed and used as a virtual assistant for patients and the public [ 1]. For example, patients pondering about whether or not to visit physicians for counseling or examination related to their minor health issues or patients wanting their prescription for chronic medication reissued might want to have AI do some of these functions so that they could save hospital visits. A similar scenario may also apply to emergency room visits. For example, if an AI system can make a suggestion using skin photographs taken with a smartphone in a child who has skin rash and fever at night regarding whether the child should visit the emergency room immediately or visit a pediatrician's office the following day, the number of children visiting the emergency room during the night might decrease. In these scenarios, AI algorithms provide information directly to the patients and enable them to take their healthcare into their own hands. The work for developing AI algorithms of this kind has lagged behind AI for clinicians and healthcare systems [ 1]. However, AI tools for directly coaching patients and the public about common chronic conditions and mild health issues will likely soon become a major topic for discussion regarding AI in medicine given the large volume that these take in healthcare. Medical professionals in the AI era have an important responsibility in clinically validating the tools, providing trustworthy information about them, and making the right decisions about their adoption in the best interest of the patients.

Medical students should acquire the appropriate knowledge and experience required for them to act as ones who take the ultimate responsibility for their patients when applying the AI technology to them. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines (which constitute the fundamentals of medical practice and are keys to understanding how to use AI for medicine), data science, biostatistics, and evidence-based medicine. Even as a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin [ 28] and even capabilities to create thoroughly validated, trustworthy information for patients and the public. While the curricula at medical schools would have yet to evolve to accommodate the educational needs sufficiently, some medical colleges in Korea (Republic of) such as University of Ulsan and Yonsei University have recently started providing AI-dedicated elective courses to the students.

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2. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N. Deep learning in medical imaging: general overview. Korean J Radiol. 2017; 18:570–584. https://doi.org/10.3348/kjr.2017.18.4.570.

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Holmes, Wayne; Bialik, Maya; Fadel, Charles; (2023) Artificial intelligence in education. In: Data ethics : building trust : how digital technologies can serve humanity. (pp. 621-653). Globethics Publications

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Abstract

The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/). Reprinted with permission of the publisher.

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Artificial intelligence in education - UCL Discovery

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UCL Discovery is UCL's open access repository, showcasing and providing access to UCL research outputs from all UCL disciplines.

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Faculty members’ use of artificial intelligence to grade student papers: a case of implications - International Journal for Educational Integrity

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This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers—including discretion, convenience,…

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Possibilities and Apprehensions in the Landscape of Artificial Intelligence in Education

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Artificial intelligence (AI) may be utilized outside of traditional computer settings and is also readily available in low-cost smart devices, making AI easily accessible to general population. Built-in capabilities for conducting complicated computer operations (edge computing), cloud-based service…

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Artificial Intelligence in Education: A Review

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The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning.

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Jian Mou is an Assistant Professor of Management Information Systems in the School of Business, Pusan National University. His research interests include e-commerce, social media, human computer interaction, trust and risk issues in electronic services, and consumer health informatics. Email:

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Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

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Reflecting on twenty years of educational research, we retrieved over 400 research article on the application of artificial intelligence (AI) and deep...

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Abstract

This chapter provides an overview of research on AI applications in higher education using a systematic review approach. There were 146 articles included for further analysis, based on explicit inclusion and exclusion criteria. The findings show that Computer Science and STEM make up the majority of disciplines involved in AI education literature and that quantitative methods were the most frequently used in empirical studies. Four areas of AI education applications in academic support services and institutional and administrative services were revealed, including profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. This chapter reflects on the challenges and risks of AI education, the lack of association between theoretical pedagogical perspectives, and the need for additional exploration of pedagogical, ethical, social, cultural, and economic dimensions of AI education.

Introduction

Software applications in the field of Artificial Intelligence (AI) in higher education (HE) have been increasing over the last few years and are starting to gain a great deal of traction. AI in education has been the subject of research for some 30 years. AI and adaptive learning technologies have made some important developments in educational technology, with adoption times between 2-3 years (Becker et al., 2019; Johnson et al., 2016). Experts predict that AI in education is set to grow by 43% by 2022, although a 2019 report predicts that in HE, AI applications that aim to enhance the teaching and learning experience are expected to grow even more significantly (Becker et al., 2019). With significant investments by private organisations like Google, which obtained the European start up Deep Mind for $400m, as well as non-profit public-private partnerships like the German Research Center for Artificial Intelligence (DFKI), there is a huge possibility that the current wave of Internet will have a greater impact on HE institutions (HEIs) (Popenici & Kerr, 2017).

Using AI in education has been the subject of huge debates for the past 30 years (Cioffi et al., 2020; Jackman et al., 2010; Jiang et al., 2017; Liu et al., 2018; O'Donovan et al., 2019; Smart & Burrell, 2015). Broadly speaking, educators have only now began to explore the potential pedagogical opportunities that AI applications can provide for supporting learners throughout the student life cycle. Although there are significant opportunities that AI can provide for higher teaching and learning, new ethical implications and risks are attached to the development of AI applications in HE. For instance, budget cuts could tempt administrators to replace the human teachers with profitable automated AI solutions. This may instil fear into faculty members, teaching assistants, student counsellors, and administrative staff that AI solutions will take their jobs. In spite of the human implications of AI (Kearney et al., 2019), AI has the potential to advance the capabilities of learning analytics, though such systems require huge amounts of data, including confidential information about students and faculty, which raises serious issues of privacy and data protection. For that reason, it would be interesting to explore the fresh ethical implications and risks in the field of AI enhanced education. Russell and Norvig (2016) emphasise that AI researchers should consider the ethical implications of their work, and thus we are interested to explore the ethical implications and risks in AI enhanced education. The purpose of this chapter is to determine whether AI learning systems will impact the human educator and educated in HE in terms of replacing their teaching and learning skills with those of a machine or application in HE, as well as the ethical implications of such applications. Owing to the growing interest of educators in the AI field, this warrants the need to review the AI literature in HE. This paper specifically addresses the following research question by means of systematic literature review (Booth et al., 2016; Liberati et al., 2009; David Moher et al., 2009):

RQ1: How is AI-enhanced education conceptualised and what are the ethical implications, challenges and risks in HE?

RQ2: How does AI enhance educational practices in HE?

Key Terms in this Chapter

Adaptive Systems: A system that possesses interacting or interdependent entities that can respond to change within a given setting.

HE: Tertiary education in which University courses are taught.

E-Learning: Teaching and learning systems driven by online and computing technologies.

Machine Learning: An application of AI that can develop computer programs with the capacity of accessing data and use it for learning.

Pedagogical Systems: Systems that can create organised, purposeful, and pedagogical or instructive influence for learners.

Artificial Intelligence: Systems performing tasks through human simulated actions, such as decision making and translation.

Intelligent Tutoring Systems: A computer that can provide instruction or feedback to learners without human instructor intervention.

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bibliographical analysis of artificial intelligence learning in higher education: is the role of the human educator and educated a thing of the past?: education

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Bibliographical Analysis of Artificial Intelligence Learning in Higher Education: Is the Role of the Human Educator and Educated a Thing of the Past?

# data[].attributes.attachment_link.description
This chapter provides an overview of research on AI applications in higher education using a systematic review approach. There were 146 articles included for further analysis, based on explicit inclusion and exclusion criteria. The findings show that Computer Science and STEM make up the majority of...

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Abstract

As online learning has been widely adopted in higher education in recent years, artificial intelligence (AI) has brought new ways for improving instruction and learning in online higher education. However, there is a lack of literature reviews that focuses on the functions, effects, and implications of applying AI in the online higher education context. In addition, what AI algorithms are commonly used and how they influence online higher education remain unclear. To fill these gaps, this systematic review provides an overview of empirical research on the applications of AI in online higher education. Specifically, this literature review examines the functions of AI in empirical researches, the algorithms used in empirical researches and the effects and implications generated by empirical research. According to the screening criteria, out of the 434 initially identified articles for the period between 2011 and 2020, 32 articles are included for the final synthesis. Results find that: (1) the functions of AI applications in online higher education include prediction of learning status, performance or satisfaction, resource recommendation, automatic assessment, and improvement of learning experience; (2) traditional AI technologies are commonly adopted while more advanced techniques (e.g., genetic algorithm, deep learning) are rarely used yet; and (3) effects generated by AI applications include a high quality of AI-enabled prediction with multiple input variables, a high quality of AI-enabled recommendations based on student characteristics, an improvement of students' academic performance, and an improvement of online engagement and participation. This systematic review proposes the following theoretical, technological, and practical implications: (1) the integration of educational and learning theories into AI-enabled online learning; (2) the adoption of advanced AI technologies to collect and analyze real-time process data; and (3) the implementation of more empirical research to test actual effects of AI applications in online higher education.

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References

* Reviewed articles (n = 32)

Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. 7th International Young Scientist Conference on Computational Science. Procedia Computer Science, 136, 16-24

Holmberg, B. (2005). Theory and practice of distance education. Routledge

Holmes, W., Bialik, M., & Fadel, C. (2019). Artifificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign

Google Scholar

Simpson, O. (2018). Supporting students in online, open and distance learning (1st ed.). Routledge

Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. (Knopf)

Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. Harvard University Press

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Acknowledgements

This work is financially supported by the National Natural Science Foundation of China, No. 62177041.

Author information

Authors and Affiliations

1. College of Education, Zhejiang University, Hangzhou, Zhejiang, 310000, China

Fan Ouyang & Luyi Zheng

2. Ocean College, Zhejiang University, Zhoushan, Zhejiang, 316021, China

Pengcheng Jiao

Corresponding author

Correspondence to Fan Ouyang.

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Appendix

Appendix

Tables 2, 3 and 4

Table 2 An overview of selected articles to address RQ1

Table 3 An overview of selected articles to address RQ2

Table 4 An overview of selected articles to address RQ3

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Ouyang, F., Zheng, L. & Jiao, P. Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Educ Inf Technol 27, 7893-7925 (2022). https://doi.org/10.1007/s10639-022-10925-9
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Keywords

Artificial Intelligence in Education

Systematic review

Online higher education

Online learning

Empirical research

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Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 - Education and Information Technologies

# data[].attributes.attachment_link.description
As online learning has been widely adopted in higher education in recent years, artificial intelligence (AI) has brought new ways for improving instruction and learning in online higher education. However, there is a lack of literature reviews that focuses on the functions, effects, and implications of applying AI in the online higher education context.

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The Benefits, Risks and Regulation of Using ChatGPT in Chinese Academia: A Content Analysis

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The Benefits, Risks and Regulation of Using ChatGPT in Chinese Academia: A Content Analysis

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Author links open overlay panel Gwo-Jen Hwang , Haoran Xie , Benjamin W. Wah , Dragan Gašević

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Abstract

The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.

Keywords

Artificial intelligence in education

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Vision, challenges, roles and research issues of Artificial Intelligence in Education

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The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED r...

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Author links open overlay panel Stephen J.H. Yang a , Hiroaki Ogata b , Tatsunori Matsui c , Nian-Shing Chen d

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Abstract

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

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Human-centered artificial intelligence in education: Seeing the invisible through the visible

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The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the mor...

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Systematic review of research on artificial intelligence applications in higher education: where are the educators?

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Autorías: Olaf Zawacki Richter, Victoria Irene Marín Juarros, Melissa Bond, Franziska Gouverneur.
Localización: International Journal of Educational Technology in Higher Education. Nº. 16, 2019.
Artículo de Revista en Dialnet.

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Abstract: Research on Artificial Intelligence in Education (AIED) has increased rapidly in recent years, so efforts are needed to understand the status of trends and their development to support and focus these trends. The specific objectives of this study are to analyze document type, source document, contributed country, language, top affiliation, sponsorship funding, top source title, subject area, research station, visualization of mapping research trends across and top 50 cited publications, reviewing some of the top-cited publications on AIED research over the last ten years using bibliometric analysis. The metadata used is the Scopus database and a mapping application using VOSviewer with 457 documents. The bibliometric results show that the development of AIED research has increased exponentially over the last five years. The most common types of documents are articles, journal document sources, and China's most productive country. English being the most significant language, the most prolific author was Kalles, D, some of the top prolific affiliates with four publications, while the most sponsored funding was the National Natural Science Foundation of China and the National Science Foundation. "Journal of Physics: Conference Series" is the primary source, the most research subject area is Computer Science, for the top-cited author is Holmes, W. Mapping of research trends shows that AIED research trends in the last ten years are: 1) it's an application to students; 2) the subject of education in engineering educations; 3) teaching methods; 4) e-learning based education; 5) education system; 6) curriculum included AI. AIED integration could revolutionize the education system.

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Artificial Intelligence in Education Research During the Last Ten Years: A Review and Bibliometric Study.

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Abstract: Research on Artificial Intelligence in Education (AIED) has increased rapidly in recent years, so efforts are needed to understand the status of trends and their development to support and focus these trends.

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Author links open overlay panel Xieling Chen a , Haoran Xie b , Di Zou c , Gwo-Jen Hwang d

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Highlights

*
Conduct a systematic review of 45 highly cited AIEd articles.

*
Identify the application and theory gaps during the rise of AI in education.

*
Establish the linkage between extant AIEd studies and future trends.

*
Clarify the definitions of AIEd from broad and narrow senses.

Abstract

Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research aims to conduct a comprehensive and systematic review of influential AIEd studies. We analyzed 45 articles in terms of annual distribution, leading journals, institutions, countries/regions, the most frequently used terms, as well as theories and technologies adopted. We also evaluated definitions of AIEd from broad and narrow perspectives and clarified the relationship among AIEd, Educational Data Mining, Computer-Based Education, and Learning Analytics. Results indicated that: 1) there was a continuingly increasing interest in and impact of AIEd research; 2) little work had been conducted to bring deep learning technologies into educational contexts; 3) traditional AI technologies, such as natural language processing were commonly adopted in educational contexts, while more advanced techniques were rarely adopted, 4) there was a lack of studies that both employ AI technologies and engage deeply with educational theories. Findings suggested scholars to 1) seek the potential of applying AI in physical classroom settings; 2) spare efforts to recognize detailed entailment relationships between learners' answers and the desired conceptual understanding within intelligent tutoring systems; 3) pay more attention to the adoption of advanced deep learning algorithms such as generative adversarial network and deep neural network; 4) seek the potential of NLP in promoting precision or personalized education; 5) combine biomedical detection and imaging technologies such as electroencephalogram, and target at issues regarding learners' during the learning process; and 6) closely incorporate the application of AI technologies with educational theories.

Keywords

Artificial intelligence in education

# data[].attributes.attachment_link.title
Application and theory gaps during the rise of Artificial Intelligence in Education

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Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research ai...

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Predicting academic success in higher education: literature review and best practices - International Journal of Educational Technology in Higher Education

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Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively use…

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Holmes, Wayne; Bialik, Maya; Fadel, Charles; (2023) Artificial intelligence in education. In: Data ethics : building trust : how digital technologies can serve humanity. (pp. 621-653). Globethics Publications

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The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/). Reprinted with permission of the publisher.

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A Synergy of Artificial Intelligence and Education in the 21 st Century Classrooms

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Artificial Intelligence now became a part and parcel of everyone's life. It has started penetrating in almost every aspects of human life including education. In this conceptual paper the author tried to explore the emergence and use of artificial intelligence in the educational arena.

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Author links open overlay panel Gwo-Jen Hwang , Shu-Yun Chien

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Abstract

The metaverse has been recognized as one of the technologies with the greatest potential today. However, the use of the metaverse for educational purposes is seldom discussed. Most educators might be unaware of the features of the metaverse, not to mention the potential applications of this emerging technology. In this position paper, we aim to provide a clear definition of the metaverse. Potential applications and research issues of the metaverse in educational settings are also presented. Moreover, the roles of AI in the metaverse as well as metaverse-based education are discussed. It is expected that, via this paper, researchers from the fields of both computer science and educational technology would have a clear picture of what the metaverse is and how it can be used for educational purposes. More importantly, it is expected that more studies related to metaverse-based education can be reported in the near future.

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Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective

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The metaverse has been recognized as one of the technologies with the greatest potential today. However, the use of the metaverse for educational purp...

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¹ Amsterdam University Medical Center, Amsterdam, Netherlands

² Department of Internal Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands

³ Section Acute Medicine, Department of Internal Medicine, Vrije Universiteit University Medical Center, Amsterdam, Netherlands

⁴ Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

*these authors contributed equally

Corresponding Author:

Ketan Paranjape, MS, MBA

Amsterdam University Medical Center

De Boelelaan 1117, 1081 HV

Amsterdam

Netherlands

Phone: 31 3174108035

Email: ketanp@alumni.gsb.stanford.edu

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.
JMIR Med Educ 2019;5(2):e16048

doi:10.2196/16048

Keywords

From clinicians' perspective there are many important trends that are affecting the way they deliver care of which the growth in medical information is alarming. It took 50 years for medical information to double in 1950. In 1980, it took 7 years. In 2010, it was 3.5 years and is now projected to double in 73 days by 2020 []. This growth is posing a challenge to health care professionals to both retain and use it effectively to practice medicine.

Artificial Intelligence in Health Care

Today, AI is being piloted in health care [] for faster and accurate diagnosis, to augment radiology [], reduce errors due to human fatigue, decrease medical costs [], assist and replace dull, repetitive, and labor-intensive tasks [], minimally invasive surgery [], and reduce mortality rates [].

Challenges With Artificial Intelligence

The rise of AI in health care and its integration into routine clinical practice is going to be a challenge. Along with changing the conventional ways physician work, the black box problem [] and liability issues [] are some of the most anticipated challenges.

Black Box

Researchers at Mount Sinai Hospital have created a deep learning algorithm that was trained on the data of 700,000 patients. This algorithm was able to predict onset of a disease such as schizophrenia with high accuracy []. This is even more impressive considering the fact that this condition is difficult to diagnose even for experts. The main problem with this algorithm is that there is no way to know how the system created this prediction and what factors were taken into consideration. This phenomenon is called the black box phenomenon. It would not be a precedent in medicine, nevertheless it is difficult to trust a system when there is no understanding on how it works. The physician needs to understand the inputs and the algorithm and interpret the AI-proposed diagnosis to ensure no errors are made. We also need to understand what the consequences or unintended side effects are of black box medicine, even when good outcomes can be demonstrated against a standard of care.

Finally, many of the AI systems attempt to mimic aspects of human and animal central nervous systems that are, at large, still a black box. In a recent paper, Zador [] argued that we have much more to learn from animal brains to unravel this phenomenon.

Privacy and Control Over Data

The development of AI algorithms almost as a rule requires data from a large number of patients. Google, for example, is using 46 billion data points collected from 216,221 adults' deidentified data over 11 combined years from 2 hospitals to predict the outcomes of hospitalized patients [,]. This raises many concerns including relating to patient privacy and control. What happens if a patient does not want to participate in a study where their information is used in algorithm development? In the European Union, the Right to be Forgotten would allow personal data to be erased when the patient has withdrawn their consent []. In situations where patient data are limited, algorithm developers train the models on synthetic or hypothetical data, with the risk of generating unsafe and incorrect treatment recommendations []. Finally, AI systems are also vulnerable to cybersecurity attacks that could cause the algorithm to misclassify medical information [].

Lack of Standards for Use of Artificial Intelligence in Patient Care and Liability

Another unresolved question related to the use of AI in health care is liability for the predictions of an algorithm. It is unclear who is liable when a patient experiences serious harm because of an inaccurate prediction. One could argue for any of the involved parties: the physician, the hospital, the company that developed the software, the person who developed the software, or even the person who delivered the data. Standards for use of AI in health care are still being developed [,]. New standards for clinical care, quality, safety, malpractice, and communication guidelines have to be developed to allow for greater use of AI. A recently launched AI system for autonomous detection of diabetic retinopathy carries medical malpractice and liability insurance [,].

As use of AI and proactive use of tools such as chatbots [] increases, physicians and patients will need to be aware of strengths and limitations of such technologies and be trained in how to effectively and safely use them [,].

How Can Artificial Intelligence Address Today's Physician Challenges?

AI could help physicians by amalgamating large amounts of data and complementing their decision-making process to identify diagnosis and recommend treatments. Physicians in turn need the ability to interpret the results and communicate a recommendation to the patient. In addition, AI could have an impact by alleviating the burden from physicians for performing day-to-day tasks []. Speech recognition could help with replacing the use of keyboards to enter and retrieve information []. Decision management can help with sifting enormous amounts of data and enable the physician to make an informed and meaningful decision [,]. Automation tools can help with managing regulatory requirements such as Protecting Access to Medicare Act and enable physicians to review the appropriate criteria before making a cost decision []. Finally, to help with the acute shortage of health care professionals, virtual agents could, in the future, help with some aspects of patient care and become a trusted source of information for patients [].

State of Medical Education Today

Table 1. Initiatives for artificial intelligence in medical education.

Duke Institute for Health Innovation

Medical students work together with data experts to develop care-enhanced technologies made for physicians

University of Florida

Radiology residents work with a technology-based company to develop computer-aided detection for mammography

Carle Illinois College of Medicine

Offers a course by a scientist, clinical scientist, and engineer to learn about new technologies

Sharon Lund Medical Intelligence and Innovation Institute

Organizes a summer course on all new technologies in health care, open to medical students

Stanford University Center for Artificial Intelligence in Medicine and Imaging

Involves graduate and postgraduate students in solving heath care problems with the use of machine learning

University of Virginia Center for Engineering in Medicine

Involves medical students in the engineering labs to create innovative ideas in health care

Another important technology-related aspect that is often overlooked in medical training is working with electronic health records (EHRs). EHRs have many benefits, such as improved patient safety, but also assist the implementation of AI in health care. AI algorithms use information from the EHR, and therefore, the knowledge on how to input unbiased data into the EHR is essential. Otherwise, the AI algorithm will likely be biased as well []. At present, training on use of EHRs for medical students and physicians is not commonly incorporated in the medical curriculum [], resulting in the medical professional using the EHR as a replacement to capture information on paper without understanding the true potential of this technology []. Training on the use of EHRs usually consists of ad hoc brief introductory courses that just teach the basic skills to use the hospital's system in practice. Quality of data and concerns on the impact of the computer on the patient-physician relationship are rarely addressed [] and the USMLE does not test on these subjects either [].

How Clinical Practice Is Changing

Physicians will have a crucial role in deciding which of these tools is best for their patients. In turn, this will likely change the physician-patient relationship []. When information processing is done mainly by computers, this highlights one of the major benefits of AI in medicine: it allows the physician to focus more on caring for and communicating with patients []. Finally, in the age of AI, "the physician should combine narrative, mechanistic and mathematical thinking in their training and consider the biopsycho-social model of the disease with the patient at its center." "Computers will never substitute for self-reflective medical expert who is aware of the strengths and limitations of human beings and of an environment characterized by information overload" [,].

What Will Be Asked From Physicians in the Future?

Practical Considerations

Some of the time that was originally spent on memorizing medical information will now have to be devoted to other skills. This will have a major impact on the way students and residents will experience their training. The system has to change in such a way that competence will no longer be judged based on factual knowledge but rather on communication skills, emotional intelligence, and knowledge on how to use computers.

With an overfull curriculum, there is limited interest in adopting new topics [], although a 2016 survey by AMA shows that 85% of physicians perceive benefits from new digital tools []. The integration of AI-oriented education into the medical curriculum will take time as the technology evolves. A new infrastructure for learning has to be introduced, and new educators from disciplines such as computer sciences, mathematics, ethnography, and economics will need to be hired. At the moment, these subjects are not even covered by the core competencies of ACGME, but these competencies "are robust enough to adapt to changing knowledge" [].

To achieve a change in curriculum, many political and bureaucratic hurdles have to be overcome. Educational systems, program structures, and objectives have to change to create new learning outcomes []. A change can only be implemented when large amount of evidence is generated. We have not reached that stage of implementing changes for AI. Furthermore, many other fields within medicine argue that they have not received the attention they deserve [,]. AI needs to prove its benefits and also justify that it is an important topic for medical curriculum over other important subjects that lack adequate medical training at present.

However, one of the most compelling arguments for the implementation of AI training in medical education is that this training will augment existing curriculum rather than replace existing coursework. When students are trained to use AI tools, focus should shift from acquiring basic knowledge on how to use the tool to a basic understanding of the underlying principles. This will enable the students to use this fundamental knowledge when current tools get outdated and new tools are introduced.

Another practical problem is that traditional medical training revolves mainly around the interactions between an attending physician and the residents or medical students. When AI is increasingly introduced into clinical practice, this could be problematic. Many senior physicians have little to no experience with AI. AI training could be delivered via Continuing Medical Education (CME) programs and might need to be also taught by educators from outside the medical community. For example, a 2-credit CME course on AI and the Future of Clinical Practice is delivered by a computational biologist and business economists [].

Framework

The traditional medical curriculum, which is mostly memorization based, must follow the transition from the information age to the age of AI. Future physicians have to be taught competence in the effective integration and utilization of information from a growing array of sources []. To embed this knowledge into medicine, it is of the essence to start introducing these concepts from the beginning of training. In many countries, a Medical College Admission Test (MCAT) has to be taken to be admitted into medical school. The current US MCAT exam, for example, focuses on biology, chemistry, physics, psychology, sociology, and reasoning []. These exams could start testing on mathematical concepts such as basis of linear algebra and calculus. These concepts are vital to the elementary understanding of AI and will set the tone for the rest of the curriculum.

During clinical rotations and residency, focus should shift toward relevant applications of AI in practice. With advancements in digital biomarkers [] and digital therapeutics [], students should also be trained in these technologies as they rely on AI. They have the potential to enable large-scale diagnostics and treatments in in-home environments in the near future []. At the end of training, the USMLE should include a substantial number of questions on data science and AI fundamentals in their final exams. Attendance of conferences on health care AI could be incentivized, so that health care professionals stay up-to-date with the latest developments. For attending physicians, extensive courses on AI and data science should be part of CME. See for more details.

Table 2. List of Continuing Medical Education programs on artificial intelligence in health care.

Program

Faculty; organization

Number of Continuing Medical Education credits

Artificial Intelligence and Machine Learning: Application in the Care of Children []

Pediatric Medicine; University of Pittsburgh School of Medicine

AI skills must also be balanced with nonanalytics and person-centered aspects of medicine to develop a more rounded doctor of the future. Other skills such as communications, empathy, shared decision making, leadership, team building, and creativity are all skills that will continue to gain importance for physicians. At the Dell Medical School at the University of Texas, Austin, the curriculum in basic sciences has been reduced in duration to accommodate training in soft skills such as leadership, creativity, and communication [].

First Steps

As not all of these interventions can be introduced simultaneously, we suggest a few first steps that will lay the foundation for the upcoming years. We suggest to start off by introducing questions on mathematical concepts into the MCAT similar to the mathematics section in the Graduate Record Examination. High quality Web-based courses on data sciences and AI fundamentals should be freely offered in the core phase of medical education. This might lead to students focusing on applications of these subjects more naturally in following years of training.

Table 3. Recommendations per stage of medical education.

Medical education stage

Recommendations

Suggested content

Introduce questions on linear algebra (vectors, linear transformations, and matrix, solutions for linear systems), calculus (limits, differential calculus, and integral calculus), probability (joint, conditional, and distribution)

Medical school-core phase

Working with medical data sets (curation, quality, provenance, integration, and governance), EHRs b, AI c fundamentals, and Ethics and Legal

Medical school-clinical phase

Familiarize with AI-based clinical applications and expand knowledge beyond basic principles of data and AI

Introduce questions on data sciences, AI, and working with EHRs

Residents

Detailed knowledge on clinical applications and attend conference in health care AI

Specialist

Stay up-to-date on data/AI through CME f credits and attend conference in health care AI

a MCAT: Medical College Admission Test.

b EHR: electronic health record.

c AI: artificial intelligence.

d MIT: Massachusetts Institute of Technology

e USMLE: United States Medical Licensing Examinations

f CME: Continuing Medical Education.

Physicians and machines working in combination have the greatest potential to improve clinical decision making and patient health outcomes []. AI can curate and process more data such as medical records, genetic reports, pharmacy notes, and environment data and in turn retain, access, and analyze more medical information. However, it cannot replace the art of caring. As AI and its application become mainstream in health care, medical students, residents, fellows, and practicing physicians need to have knowledge of AI, data sciences, EHR fundamentals, and ethics and legal issues concerning AI. Medical schools will need to include them as part of the curriculum. A staged approach to educating the medical student through their journey is recommended.

AI will enable faster and accurate diagnosis, augment radiology, reduce errors due to human fatigue, decrease medical costs, assist and replace dull, repetitive, and labor-intensive tasks, minimally invasive surgery, and reduce mortality rates.

Conflicts of Interest

KP has written this paper as part of his PhD studies. He is a vice president at Roche. There is no conflict of interest with his employment at Roche. None of the rest of the authors declare any conflicts of interest.

‎

AI: artificial intelligence

ACGME: Accreditation Council for Graduating Medical Education

AMA: American Medical Association

CME: Continuing Medical Education

EHR: electronic health record

MCAT: Medical College Admission Test

NLP: natural language processing

USMLE: United States Medical Licensing Examinations

Edited by G Eysenbach; submitted 09.09.19; peer-reviewed by C Khoury, A Palanica; comments to author 11.10.19; revised version received 16.10.19; accepted 20.10.19; published 03.12.19

©Ketan Paranjape, Michiel Schinkel, Rishi Nannan Panday, Josip Car, Prabath Nanayakkara. Originally published in JMIR Medical Education (http://mededu.jmir.org), 03.12.2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on http://mededu.jmir.org/, as well as this copyright and license information must be included.

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.
JMIR Med Educ 2019;5(2):e16048

doi:10.2196/16048

Keywords

From clinicians' perspective there are many important trends that are affecting the way they deliver care of which the growth in medical information is alarming. It took 50 years for medical information to double in 1950. In 1980, it took 7 years. In 2010, it was 3.5 years and is now projected to double in 73 days by 2020 []. This growth is posing a challenge to health care professionals to both retain and use it effectively to practice medicine.

Artificial Intelligence in Health Care

Today, AI is being piloted in health care [] for faster and accurate diagnosis, to augment radiology [], reduce errors due to human fatigue, decrease medical costs [], assist and replace dull, repetitive, and labor-intensive tasks [], minimally invasive surgery [], and reduce mortality rates [].

Challenges With Artificial Intelligence

The rise of AI in health care and its integration into routine clinical practice is going to be a challenge. Along with changing the conventional ways physician work, the black box problem [] and liability issues [] are some of the most anticipated challenges.

Black Box

Researchers at Mount Sinai Hospital have created a deep learning algorithm that was trained on the data of 700,000 patients. This algorithm was able to predict onset of a disease such as schizophrenia with high accuracy []. This is even more impressive considering the fact that this condition is difficult to diagnose even for experts. The main problem with this algorithm is that there is no way to know how the system created this prediction and what factors were taken into consideration. This phenomenon is called the black box phenomenon. It would not be a precedent in medicine, nevertheless it is difficult to trust a system when there is no understanding on how it works. The physician needs to understand the inputs and the algorithm and interpret the AI-proposed diagnosis to ensure no errors are made. We also need to understand what the consequences or unintended side effects are of black box medicine, even when good outcomes can be demonstrated against a standard of care.

Finally, many of the AI systems attempt to mimic aspects of human and animal central nervous systems that are, at large, still a black box. In a recent paper, Zador [] argued that we have much more to learn from animal brains to unravel this phenomenon.

Privacy and Control Over Data

The development of AI algorithms almost as a rule requires data from a large number of patients. Google, for example, is using 46 billion data points collected from 216,221 adults' deidentified data over 11 combined years from 2 hospitals to predict the outcomes of hospitalized patients [,]. This raises many concerns including relating to patient privacy and control. What happens if a patient does not want to participate in a study where their information is used in algorithm development? In the European Union, the Right to be Forgotten would allow personal data to be erased when the patient has withdrawn their consent []. In situations where patient data are limited, algorithm developers train the models on synthetic or hypothetical data, with the risk of generating unsafe and incorrect treatment recommendations []. Finally, AI systems are also vulnerable to cybersecurity attacks that could cause the algorithm to misclassify medical information [].

Lack of Standards for Use of Artificial Intelligence in Patient Care and Liability

Another unresolved question related to the use of AI in health care is liability for the predictions of an algorithm. It is unclear who is liable when a patient experiences serious harm because of an inaccurate prediction. One could argue for any of the involved parties: the physician, the hospital, the company that developed the software, the person who developed the software, or even the person who delivered the data. Standards for use of AI in health care are still being developed [,]. New standards for clinical care, quality, safety, malpractice, and communication guidelines have to be developed to allow for greater use of AI. A recently launched AI system for autonomous detection of diabetic retinopathy carries medical malpractice and liability insurance [,].

As use of AI and proactive use of tools such as chatbots [] increases, physicians and patients will need to be aware of strengths and limitations of such technologies and be trained in how to effectively and safely use them [,].

How Can Artificial Intelligence Address Today's Physician Challenges?

AI could help physicians by amalgamating large amounts of data and complementing their decision-making process to identify diagnosis and recommend treatments. Physicians in turn need the ability to interpret the results and communicate a recommendation to the patient. In addition, AI could have an impact by alleviating the burden from physicians for performing day-to-day tasks []. Speech recognition could help with replacing the use of keyboards to enter and retrieve information []. Decision management can help with sifting enormous amounts of data and enable the physician to make an informed and meaningful decision [,]. Automation tools can help with managing regulatory requirements such as Protecting Access to Medicare Act and enable physicians to review the appropriate criteria before making a cost decision []. Finally, to help with the acute shortage of health care professionals, virtual agents could, in the future, help with some aspects of patient care and become a trusted source of information for patients [].

State of Medical Education Today

Table 1. Initiatives for artificial intelligence in medical education.

Duke Institute for Health Innovation

Medical students work together with data experts to develop care-enhanced technologies made for physicians

University of Florida

Radiology residents work with a technology-based company to develop computer-aided detection for mammography

Carle Illinois College of Medicine

Offers a course by a scientist, clinical scientist, and engineer to learn about new technologies

Sharon Lund Medical Intelligence and Innovation Institute

Organizes a summer course on all new technologies in health care, open to medical students

Stanford University Center for Artificial Intelligence in Medicine and Imaging

Involves graduate and postgraduate students in solving heath care problems with the use of machine learning

University of Virginia Center for Engineering in Medicine

Involves medical students in the engineering labs to create innovative ideas in health care

Another important technology-related aspect that is often overlooked in medical training is working with electronic health records (EHRs). EHRs have many benefits, such as improved patient safety, but also assist the implementation of AI in health care. AI algorithms use information from the EHR, and therefore, the knowledge on how to input unbiased data into the EHR is essential. Otherwise, the AI algorithm will likely be biased as well []. At present, training on use of EHRs for medical students and physicians is not commonly incorporated in the medical curriculum [], resulting in the medical professional using the EHR as a replacement to capture information on paper without understanding the true potential of this technology []. Training on the use of EHRs usually consists of ad hoc brief introductory courses that just teach the basic skills to use the hospital's system in practice. Quality of data and concerns on the impact of the computer on the patient-physician relationship are rarely addressed [] and the USMLE does not test on these subjects either [].

How Clinical Practice Is Changing

Physicians will have a crucial role in deciding which of these tools is best for their patients. In turn, this will likely change the physician-patient relationship []. When information processing is done mainly by computers, this highlights one of the major benefits of AI in medicine: it allows the physician to focus more on caring for and communicating with patients []. Finally, in the age of AI, "the physician should combine narrative, mechanistic and mathematical thinking in their training and consider the biopsycho-social model of the disease with the patient at its center." "Computers will never substitute for self-reflective medical expert who is aware of the strengths and limitations of human beings and of an environment characterized by information overload" [,].

What Will Be Asked From Physicians in the Future?

Practical Considerations

Some of the time that was originally spent on memorizing medical information will now have to be devoted to other skills. This will have a major impact on the way students and residents will experience their training. The system has to change in such a way that competence will no longer be judged based on factual knowledge but rather on communication skills, emotional intelligence, and knowledge on how to use computers.

With an overfull curriculum, there is limited interest in adopting new topics [], although a 2016 survey by AMA shows that 85% of physicians perceive benefits from new digital tools []. The integration of AI-oriented education into the medical curriculum will take time as the technology evolves. A new infrastructure for learning has to be introduced, and new educators from disciplines such as computer sciences, mathematics, ethnography, and economics will need to be hired. At the moment, these subjects are not even covered by the core competencies of ACGME, but these competencies "are robust enough to adapt to changing knowledge" [].

To achieve a change in curriculum, many political and bureaucratic hurdles have to be overcome. Educational systems, program structures, and objectives have to change to create new learning outcomes []. A change can only be implemented when large amount of evidence is generated. We have not reached that stage of implementing changes for AI. Furthermore, many other fields within medicine argue that they have not received the attention they deserve [,]. AI needs to prove its benefits and also justify that it is an important topic for medical curriculum over other important subjects that lack adequate medical training at present.

However, one of the most compelling arguments for the implementation of AI training in medical education is that this training will augment existing curriculum rather than replace existing coursework. When students are trained to use AI tools, focus should shift from acquiring basic knowledge on how to use the tool to a basic understanding of the underlying principles. This will enable the students to use this fundamental knowledge when current tools get outdated and new tools are introduced.

Another practical problem is that traditional medical training revolves mainly around the interactions between an attending physician and the residents or medical students. When AI is increasingly introduced into clinical practice, this could be problematic. Many senior physicians have little to no experience with AI. AI training could be delivered via Continuing Medical Education (CME) programs and might need to be also taught by educators from outside the medical community. For example, a 2-credit CME course on AI and the Future of Clinical Practice is delivered by a computational biologist and business economists [].

Framework

The traditional medical curriculum, which is mostly memorization based, must follow the transition from the information age to the age of AI. Future physicians have to be taught competence in the effective integration and utilization of information from a growing array of sources []. To embed this knowledge into medicine, it is of the essence to start introducing these concepts from the beginning of training. In many countries, a Medical College Admission Test (MCAT) has to be taken to be admitted into medical school. The current US MCAT exam, for example, focuses on biology, chemistry, physics, psychology, sociology, and reasoning []. These exams could start testing on mathematical concepts such as basis of linear algebra and calculus. These concepts are vital to the elementary understanding of AI and will set the tone for the rest of the curriculum.

During clinical rotations and residency, focus should shift toward relevant applications of AI in practice. With advancements in digital biomarkers [] and digital therapeutics [], students should also be trained in these technologies as they rely on AI. They have the potential to enable large-scale diagnostics and treatments in in-home environments in the near future []. At the end of training, the USMLE should include a substantial number of questions on data science and AI fundamentals in their final exams. Attendance of conferences on health care AI could be incentivized, so that health care professionals stay up-to-date with the latest developments. For attending physicians, extensive courses on AI and data science should be part of CME. See for more details.

Table 2. List of Continuing Medical Education programs on artificial intelligence in health care.

Program

Faculty; organization

Number of Continuing Medical Education credits

Artificial Intelligence and Machine Learning: Application in the Care of Children []

Pediatric Medicine; University of Pittsburgh School of Medicine

AI skills must also be balanced with nonanalytics and person-centered aspects of medicine to develop a more rounded doctor of the future. Other skills such as communications, empathy, shared decision making, leadership, team building, and creativity are all skills that will continue to gain importance for physicians. At the Dell Medical School at the University of Texas, Austin, the curriculum in basic sciences has been reduced in duration to accommodate training in soft skills such as leadership, creativity, and communication [].

First Steps

As not all of these interventions can be introduced simultaneously, we suggest a few first steps that will lay the foundation for the upcoming years. We suggest to start off by introducing questions on mathematical concepts into the MCAT similar to the mathematics section in the Graduate Record Examination. High quality Web-based courses on data sciences and AI fundamentals should be freely offered in the core phase of medical education. This might lead to students focusing on applications of these subjects more naturally in following years of training.

Table 3. Recommendations per stage of medical education.

Medical education stage

Recommendations

Suggested content

Introduce questions on linear algebra (vectors, linear transformations, and matrix, solutions for linear systems), calculus (limits, differential calculus, and integral calculus), probability (joint, conditional, and distribution)

Medical school-core phase

Working with medical data sets (curation, quality, provenance, integration, and governance), EHRs b, AI c fundamentals, and Ethics and Legal

Medical school-clinical phase

Familiarize with AI-based clinical applications and expand knowledge beyond basic principles of data and AI

Introduce questions on data sciences, AI, and working with EHRs

Residents

Detailed knowledge on clinical applications and attend conference in health care AI

Specialist

Stay up-to-date on data/AI through CME f credits and attend conference in health care AI

a MCAT: Medical College Admission Test.

b EHR: electronic health record.

c AI: artificial intelligence.

d MIT: Massachusetts Institute of Technology

e USMLE: United States Medical Licensing Examinations

f CME: Continuing Medical Education.

Physicians and machines working in combination have the greatest potential to improve clinical decision making and patient health outcomes []. AI can curate and process more data such as medical records, genetic reports, pharmacy notes, and environment data and in turn retain, access, and analyze more medical information. However, it cannot replace the art of caring. As AI and its application become mainstream in health care, medical students, residents, fellows, and practicing physicians need to have knowledge of AI, data sciences, EHR fundamentals, and ethics and legal issues concerning AI. Medical schools will need to include them as part of the curriculum. A staged approach to educating the medical student through their journey is recommended.

AI will enable faster and accurate diagnosis, augment radiology, reduce errors due to human fatigue, decrease medical costs, assist and replace dull, repetitive, and labor-intensive tasks, minimally invasive surgery, and reduce mortality rates.

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Introducing Artificial Intelligence Training in Medical Education

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Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction.

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An Introduction to Key Technology in Artificial Intelligence and big Data Driven e-Learning and e-Education - Mobile Networks and Applications

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Age of artificial intelligence and big data comes rapidly with fast increment of computer and communication technology, and the new technology improvements bring new educational age [ 1]. Mobile education and fragmentation of education become new trends. Moreover, artificial intelligence and mobile network technology in education is also becoming a hot research domain [ 2].

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Nykan Mirchi,

Vincent Bissonnette,

Recai Yilmaz,

Nicole Ledwos,

Alexander Winkler-Schwartz,

Rolando F. Del Maestro

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Abstract

Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms' decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4-6 residents) and 22 novice participants (10 PGY 1-3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.

Citation: Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF (2020) The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS ONE 15(2): e0229596. https://doi.org/10.1371/journal.pone.0229596

Editor: Paweł Pławiak, Politechnika Krakowska im Tadeusza Kosciuszki, POLAND

Received: July 4, 2019; Accepted: February 10, 2020; Published: February 27, 2020

Copyright: © 2020 Mirchi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All code is available at ( https://github.com/Ai-SimCenter/Virtual-Operative-Assistant/).

Funding: This work was supported by the Di Giovanni Foundation. The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: A patent entitled: METHOD AND SYSTEM FOR GENERATING A TRAINING PLATFORM (Patent Application US 62/821,091) has been filed which outlines the concept of the Virtual Operative Assistant. Each of the 6 authors of this paper is an equal co-inventor of the patent. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

Advances in technology have allowed digital platforms to become integrated into educational programs. The use of these technologies allows automation of traditional forms of teaching while also re-defining valued educational goals.[ 1] Digital technologies can quantitate skill performance, which, when analysed with artificial intelligence can result in new perspectives on psychomotor expertise and its composites. This is particularly useful for understanding complex tasks that need to be deconstructed into small components to provide an appropriate scaffold for learning. While artificial intelligence methodologies have been employed to assess skill level on simulated tasks, efforts are needed to enhance the understanding of the classification mechanisms utilized.

The application of artificial intelligence (AI) and machine learning in various fields has substantially facilitated the evaluation of large and multivariate datasets.[ 2, 3] Several types of algorithms fit under the umbrella of machine learning. Recent literature and newer applications of AI are also focused on artificial neural networks and deep learning, subsets of AI inspired by the biological neural system.[ 4- 7] Although these models have shown significant potential in economics, finance, and medical applications, a common criticism of deep learning is that its decision-making process is a "black box".[ 6] Basically, this means that it is difficult to understand how an algorithm makes a particular decision. This is problematic in the context of education because transparency and trust are vital components of ensuring a successful connection between teacher and learner.[ 8] Transparency is also important for developing and implementing appropriate grading schemes and feedback mechanisms. Without such mechanisms, students report negative emotions, such as frustration and discomfort, when using technology for higher (post-secondary) education.[ 9] The development of a feedback system powered by AI, and based on transparency, addresses some of these issues.

The employment of AI methodology for identifying components of expertise lends itself well for the understanding and teaching of complex tasks.[ 8] Virtual reality surgical simulators generate large amounts of data from each individual's specific operative performance.[ 5, 10, 11] This data can be analysed and distilled to quantitate performance and provide automated feedback to the operator. This not only provides an efficient way to understand expertise, but it can also uncover unique features of skill that may have gone previously unrecognized.[ 5, 11, 12] In this study, we outline an educational platform (The Virtual Operative Assistant) for complex technical task enhancement based on explainable machine learning, and validate this system with a complex neurosurgical skill. The development of an objective and automated feedback system would allow trainees to practice and perfect surgical skills before operating on patients.

Although a number of virtual reality surgical training simulator platforms such as the NeuroVR (previously known as the NeuroTouch)[ 13] and SimOrtho[ 14] have been developed, progress in the implementation of simulator feedback platforms has been problematic. In the context of surgery, visual rating scales such as the Objective Structured Assessment of Technical Skills (OSATS) tool are considered the gold standard for the assessment of simulated tasks.[ 15] While this method has been shown to be valid and reliable for some surgical tasks,[ 16] it relies on the presence of examiners, and is thus prone to subjectivity and it is highly resource dependent. This can be problematic for trainees as they are constantly relying on the presence of a limited number of surgical educators. Hence, there is a need for an automated and more objective method of providing feedback for simulation-based training. Our group has demonstrated that performance metrics from a virtual reality simulator correlate with current visual rating scales (like the OSATS), while offering additional advantages such as the assessment of force application which cannot be assessed visually.[ 17] Our group has also developed best practices guidelines for utilizing AI in surgical simulation studies based on systematic literature search.[ 18] The Virtual Operative Assistant outlined in this study offers objective and automated feedback for the learner based on performance metrics from virtual reality simulators, allowing for an enhanced understanding of the critical components of expert performance.

The framework presented here aims to 1) introduce a novel and flexible method for teaching in simulation-based training in surgery and medicine, 2) validate the method by developing the Virtual Operative Assistant, a virtual training platform for a complex neurosurgical procedure.

Our framework was successfully validated with a linear support vector machine (SVM) algorithm, capable of classifying two groups of different skill levels (skilled and novice) according to various metrics of surgical performance in a virtual reality brain tumor resection task.

We present our study by first, introducing data acquisition from a VR simulator, followed by the steps taken to create and select metrics of performance. A thorough explanation of the machine learning algorithm training and testing process is then provided. Finally, we discuss the steps to extract individual metric scores and present them to a new participant using the Virtual Operative Assistant.

Methods

This study was approved by the McGill University Health Centre Research Ethics Board, Neurosciences-Psychiatry. All participants whose data was used to train the machine learning model signed an approved written consent form.

Building an explainable machine learning powered teaching platform can be achieved following the framework in Fig 1. The system relies on a single processing unit known as the perceptron.[ 19] Building from this, the authors outline the rationale for employing the perceptron (also known as the linear Support Vector Machine) in this study in the S1 Appendix in S1 File.

The top section provides an overview of standard machine learning methodology to obtain a predictive model. The bottom section harvests the power of the machine learning model for education. The simulation task generates raw data, which can be manipulated to create metrics of performance. Using algorithms, statistical methods, or expert opinions, metrics are selected based on their ability to differentiate between two or more groups. The selected metrics are fed to a machine learning algorithm for training and testing. A final predictive model can be selected based on predictive accuracy to build the Virtual Operative Assistant. Upon recruitment of new participants, their metrics of performance can be calculated and normalized. These are then fed to the Virtual Operative Assistant to provide a group classification (e.g. skilled or novice) as well as an individual breakdown of metric performance. The feedback reinforces positive behaviour while providing detailed information on which behaviours to improve. The Virtual Operative Assistant is an iterative program optimized for user learning.

Raw data acquisition

The simulated medical or surgical task generates a large amount of data regarding how a user is interacting with the simulated scenario. This data can include basic measurements such as positioning of a surgical tool, and more complex measures such as tool rotation, forces applied on anatomical structures and volume of anatomical structures removed.

Metric generation

The generation of performance metrics is accomplished utilizing three main methods. First, one can consult with experts in the field to create metrics representative of current measures of expertise. In the surgical field, for example, expert surgeons can be consulted to choose metrics that they believe reflect surgical performance in the surgical simulated task. Second, one can consult previous simulation studies and utilize these previously developed metrics. It is also useful to try to develop unique and previously unknown metrics able to differentiate expertise. In the surgical context, trainee instrument force application is difficult to assess by surgeon instructors. Computer platforms are capable of extracting both positional and quality components of trainee force utilization [ 20, 21] allowing quantification of these novel surgical performance metrics. This mechanism of metric generation may offer novel insight on the underlying constructs of expertise for the surgical scenario utilized. Efforts should be made to generate as many different metrics as possible, to try and capture the multiple facets of a given technical skill.

Metric selection

Once metrics have been generated, the set of metrics must be narrowed to only those that are significant to be fed to the machine learning algorithm.[ 22] Several techniques can be used to achieve metric selection, although some of the most common include forward or backward feature selection. Forward feature selection involves an iterative process whereby metrics are added one by one and the algorithm's predictive performance is tested for each iteration.[ 22] The final metric set is defined as the set where the highest accuracy is achieved. Backward feature selection starts with all the metrics and gradually removes one metric at a time while testing the algorithm's performance.[ 22] The optimal set of metrics is defined by the point where the highest accuracy is reached. Since feature selection is an evolving research field, continually consulting the appropriate literature aids in selecting the optimal technique(s) for the simulation scenarios utilized.[ 23] This study employs a custom algorithm which combines forward and backward feature selection.[ 12]

Machine learning algorithm training & testing

Once final metrics have been selected, they can be used to train supervised machine learning algorithms. The data should normalized to ensure all metrics are on the same scale. The dataset must be labelled (e.g. for medical simulation: expert/skilled or novice/less skilled) and contain a relatively large number of users. If the dataset is large enough, the labelled data should be split into two groups, one used for training and the other used for testing.[ 24, 25] The training set is used to train the algorithm to recognize different classes.[ 24, 25] An iterative cost reduction approach is employed during training,[ 26] whereby the algorithm alters values of the weights ( θ) for each metric in order to reach the greatest predictive accuracy.

Once the model's predictive power has been optimized, it is important to test the model on a new (previously unseen) dataset.[ 25] This step is important in order to detect overfitting of the model. Overfitting occurs when the model fits the training data too closely and cannot be generalized to new data with similar accuracy.[ 27] These models will perform poorly.

Model selection

Once a model has been selected, it can be saved along with all its parameters. This model will be the core of the Virtual Operative Assistant.

New user classification

New users can then be recruited to perform a task on the medical or surgical simulator. Following the previously described methods, raw data must be extracted from the simulator following completion of the simulated task. Using the same coded instructions that were previously used to generate metrics, the final metrics can be calculated for the new user. It is important to normalize the new data using the same methodology as the training data. A common normalization method is to calculate the z-score to ensure that all metrics are on the same relative scale.[ 28]

Once each metric has been normalized, they can be inputted into the Virtual Operative Assistant's model. If the output of the model is positive, the new user belongs to class Y = +1. In the case of surgical simulation, this corresponds to a skilled participant. However, if the output of the model is negative, the new user would belong to class Y = -1. In surgical simulation, this corresponds to a novice participant. In addition, the probability of a user belonging to each class can be extracted to reflect expertise as a gradient rather than binary classes. This scoring method is more extensively explained in the S1 Appendix in S1 File.

Individual metric scoring

The output of the Virtual Operative Assistant model relies on a series of inputs and corresponding weights. A unique advantage of the Virtual Operative Assistant is that it offers insight as to how each metric contributes to the model's decision making. As outlined in the S1 Appendix in S1 File, a positive model output is optimized when each combination of metrics multiplied by their respective weight is also positive. Specific examples are discussed in the S2 Appendix in S1 File. This feature offers an in-depth insight into the algorithm to provide individualized performance feedback through the Virtual Operative Assistant.

Framework validation

The model employed in this study to validate the Virtual Operative Assistant is a linear Support Vector Machine (SVM) developed in a previous study to evaluate a virtual reality brain tumor resection task on the NeuroVR simulation platform (CAE Healthcare, Montreal, Quebec, Canada).[ 12] The simulated scenario involves the removal of a subpial brain tumor using a simulated ultrasonic aspirator in the dominant hand, and a simulated bipolar in the non-dominant hand.[ 12] The task required the tumor to be removed completely while minimizing bleeding and healthy tissue damage. This model was originally developed to differentiate 4 groups of expertise. Since the present framework is designed for 2 groups the model was adapted to differentiate two groups, a skilled and a novice group. The participant demographics are described in Table 1. Fifty participants were recruited including 14 staff neurosurgeons, 4 neurosurgical fellows, 10 senior residents (postgraduate year 4 to 6), 10 junior residents (postgraduate year 1 to 3) and 12 medical students. The skilled group consisted of 28 participants including neurosurgeons, neurosurgical fellows and senior residents of postgraduate year 4 (PGY-4) and above. While the novice group consisted of 22 participants including junior residents PGY-3 and below and medical students. Two-hundred and seventy metrics of performance were created using the data recorded by the simulator.[ 12] Four final metrics were selected by using an iterative loop algorithm previously developed by our research group.[ 12] The metrics were normalized by calculating the z-scores and a support vector machine algorithm was trained using this data and the leave-one-out cross-validation accuracy was 92% (sensitivity: 100%; specificity: 82%). The confusion matrix in Fig 2 illustrates the classification results. No skilled participants were classified as novice and 4 novice participants were classified as skilled. The Virtual Operative Assistant was developed in Matlab R2018b. All code is publicly available at https://github.com/Ai-SimCenter/Virtual-Operative-Assistant/.

The algorithm correctly classified all skilled participants while correctly classifying 18 of the 22 novice participants (82%).

Results

Individualized metric performance feedback

The Virtual Operative Assistant was built and then validated with a complex virtual reality neurosurgical procedure involving the bimanual subpial removal of a lesion in the cerebral cortex which is commonly used in brain tumor and epilepsy surgery.[ 12] The S1 Video in the supplemental information demonstrates the simulated scenario. The system provides a metric-wise assessment of performance depending on the weight of each metric as described in the Individual Metric Scoring section above. If the weight is positive, the trainee should aim for a positive metric. Importantly, as these metrics are normalized through z-score calculation, this indicates that the trainee should aim to achieve a value higher than the mean of the training data to gain a positive normalized metric. If the weight is negative, the trainee should aim for a negative (or less positive) metric. Following the same logic, this corresponds to a value lower than the mean for the training data.

The neurosurgical model was composed of 4 metrics, each with a negative weight. These include 2 safety and 2 movement metrics as illustrated in Table 2. The weights provided information on the relative importance of each metric on the model's decision-making process.[ 24, 29] A metric whose corresponding weight has a larger magnitude will play a greater role in the algorithm's decision-making process.[ 29] Using this information, the metrics may be ranked to selectively train new users on the most important metrics first (briefly outlined in Teaching with the Virtual Operative Assistant section). Alternatively, the metric weights may simply be incorporated as part of a scoring system. Although the algorithm ranked the "Instrument tip distance" metric as most important, the authors opted to consult with experienced neurosurgeons to determine which metrics would be most important to teach first. Upon consultation, the safety metrics were deemed to be more important than movement metrics. This was done to ensure that the Virtual Operative Assistant resembled the current focus of neurosurgical education.[ 30]

Teaching with the Virtual Operative Assistant

To demonstrate the teaching potential of the framework, a teaching paradigm for a simulated neurosurgical scenario is illustrated in Fig 3.

(A) The trainee performs a simulated subpial tumor resection scenario on the NeuroVR (CAE Healthcare, Montreal, Quebec, Canada) platform using a simulated ultrasonic aspirator in the trainee's dominant hand and a simulated bipolar in the non-dominant hand. (B) The scenario involves removal of a cortical tumor (yellow) with minimal damage to healthy brain regions (white). (C) Upon completion of the simulated task, the data is automatically saved and uploaded to the Virtual Operative Assistant software to provide instant feedback on two monitors.

Following the principles of cognitive load theory,[ 31] the Virtual Operative Assistant aims to limit the amount of information immediately provided to the user to facilitate learning. This is achieved by categorizing the metrics into groups, a process also known as chunking,[ 32] while providing feedback in a stepwise manner. This approach can be visualised in Fig 4.

User received feedback on performance on two monitors. (A) The first screen informs the trainee of their classification along with a corresponding percentage for each class. (B) The second screen provides individualized feedback on 2 safety metrics. Metrics are accompanied with a positive statement (if competency achieved) or instructions to improve (if failed to achieve competency). Overall written and auditory feedback is provided on the second monitor. If the trainee has not achieved competency in all safety metrics (top row), the trainee cannot proceed further and must redo the scenario. If the trainee has achieved competency in all safety metrics (bottom row), the trainee can move on to step 2. (C) The third screen provides individualized feedback on 2 instrument motion metrics, accompanied by statements on their performance.

Upon completion of the simulated task, the user receives feedback on two monitors, divided into two steps, each containing two metrics. The machine learning algorithm initially computes a classification as "skilled" or "novice". As expertise is often regarded as a gradient rather than a binary classification, the user's classification is presented as a percentage of each class. This was followed by a metric-wise assessment. The first step includes metrics related to safety, while the second step contains metrics related to movements of the simulated instruments. Red zones indicate a novice performance for the respective metric, whereas green zones indicate a skilled performance. The boundary between the two zones is defined as the mean of all training data (z-score of 0). The paradigm is iterative, where the user must achieve full competency in all metrics of Step 1, in order to move on to Step 2. If the user fails to do so, this individual must repeat the task until competency is achieved. This follows the mastery learning model[ 33] by encouraging the trainee to master a component of the task before moving on. As highlighted by Block and Burns,[ 33] this technique ensures that the trainee has relevant skills to make subsequent steps of training more achievable, while imposing a realistic challenge for the trainee. A summary of the user's performance is also provided at every iteration below each metric. The feedback reinforces the user's positive behaviours where competency was achieved, while also addressing the metrics where the user performed less well. The summary incorporates automated auditory and video-based instruction specific to the psychomotor metric issues identified. This feature is designed to mimic current operating room surgeon instructor feedback strategies while allowing for self-guided learning. The text feedback provides a breakdown of positive behaviours and behaviours to improve. The auditory components provide more holistic feedback imitating what the learner would receive from the surgeon instructor in the operating room. The video-based feedback allows the trainee to compare their performance to that of skilled operator. We recognize that some individual psychomotor metrics may be difficult to teach and for the trainee to learn without special instruction. In the operating room a skilled surgeon instructor would first perform the complex task carefully outlining the critical steps to the learner. To model this component of operating room instruction the Virtual Operative Assistant incorporates a video for each metric illustrating how a skilled surgeon would perform the respective metric.

Discussion

In this study, we investigated a transparency framework for machine learning to create an educational platform for complex psychomotor tasks. We achieved both aims of the study by 1) successfully introducing the steps of a novel teaching framework for simulation-based training, and 2) successfully validating our framework with a complex neurosurgical task through the Virtual Operative Assistant. We relied on a strong fidelity between the educational platform (Virtual Operative Assistant) and how the machine learning algorithm computes a trainee's performance in a task.

Transparency in education

Digital platforms have become increasingly common components of educational paradigms.[ 34] However, research involving the use of technology in higher education has identified neglect, frustration, uncertainty, need for confirmation and discomfort to be the primary negative emotions experienced by students.[ 9] These issues are believed to be due to "lack of feedback or faulty interactions".[ 9] The concept of transparency in grading is central to addressing these issues. The transparency framework developed in this study directly addresses these challenges.

Benefits of the Virtual Operative Assistant

The Virtual Operative Assistant offers many advantages over non-AI methods of teaching in medical simulation. It allows educators to identify individual components of psychomotor expertise in tasks too multifaceted for instructors to adequately appreciate without this technology. The complexity of surgery makes it an ideal educational paradigm to apply these systems.[ 35] Teaching in surgery is largely reliant on the apprenticeship model whereby the knowledge of a skill (know-how) is assumed to be learned through observation and graduated active responsibility in the operating room.[ 36] Therefore, a significant part of knowledge and skill acquisition also occurs through experiential learning. Trainees are often expected to learn a combination of complex skills to achieve expertise, without a comprehensive understanding of the individual components of holistic surgical competency. This form of training allows non-experts to automatically perform a given task after a number of repetitions, but without the inherent ability to shift between the automatic state and a more effortful state (i.e. a state where every motion is the result of a thinking process) capable of dealing with complications. This is analogous to driving, where most experienced drivers are experts and drive their car in an effortless automatic manner. A sudden unexpected event such as losing control of the vehicle on ice forces the experienced driver to dissociate from this automatic state to a more effortful state to deal with unexpected events. Moulton et al. discuss the importance of decoding the automatic as an essence to become an expert in a task.[ 37] The framework presented in this study allows medical educators to gain insight into the significant components that make up their skills. The Virtual Operative Assistant not only reveals significant metrics, but also incorporates the relationship between different metrics rather than assessing each metric individually. This allows trainees to understand how good performance in one metric may be compensating for a poor performance in another. The Virtual Operative Assistant outlines this issue and guides trainees along a path to achieve competency in all critical metrics. The Virtual Operative Assistant is designed to mimic real life training based on the well-established apprenticeship model employed to train surgeons and medical trainees. The system provides a similar feedback model as currently utilized by expert instructors to less skilled learners in the clinical or operating environment. Auditory feedback with a human voice reinforces components of the task that were correctly done and explains other components that require improvements. The video-based feedback demonstrates and reinforces the technical psychomotor skills necessary for attaining successful competence in each metric. This mimics real life experience where the surgeon will perform and thus outline to the learner the actual bimanual psychomotor skills necessary for safe operative outcomes. The Virtual Operative Assistant is an objective assessment tool which allows for self-guided and standardized medical or surgical training amongst learners. Studies have utilized machine learning to assess 2D visual images of operative procedures[ 38] and neuroanatomy.[ 39] However these systems do not allow for an assessment of quantitative instrument force application and instrument movements in a 3D operative environment nor provide verbal, visual and auditory feedback which may enhance the learning opportunities for the trainee.[ 10, 21, 40]

Flexibility of the transparency framework

The design and presentation of information are vital components of digital educational platforms to optimize learning. Norman describes design in three different levels: visceral, behavioural, and reflective.[ 41] When applied to the Virtual Operative Assistant, visceral refers to the look of the platform, behavioural refers to the manner by which users and computers interact with each other, and reflective involves impression and judgement. The Virtual Operative Assistant addresses the behavioural level by providing feedback, and the reflective level by mimicking familiar feedback through audition and visual components.[ 9, 42] Studies across a variety of fields have proven the benefit of feedback to enhance skill acquisition and overall performance.[ 43] The framework is capable of adapting to the best practices of adult learning theories. As educational theories evolve, the information generated by the framework may be recombined in new ways taking advantage of systems available in the outlined Virtual Operative Assistant. The transparency framework presented places no restrictions on the platform interface. We present one type of interface in our Virtual Operative Assistant adapted to a neurosurgical scenario, however, the transparency framework is designed to allow new users to present the information in any way they like. This principle allows for flexibility to meet the visceral level of design. Although we present the transparency framework as applied to virtual reality surgical simulation, it can be employed to teach any form of complex task where a multivariate dataset can be obtained from experts and non-experts.

Limitations

Limitations exist when utilizing the AI-powered feedback platforms. First, the framework was developed with a Support Vector Machines (SVM) algorithm distinguishing between two groups of expertise. Machine learning algorithms have the potential to distinguish between more than two groups of expertise.[ 12, 44] Studies are presently ongoing to adapt the framework to multi-layered perceptron such as artificial neural networks and to modify the framework for multiclass classification.[ 5] Second, the machine learning model is composed of metrics, weights, and a bias (extensively discussion in the S1 Appendix in S1 File). The bias is a weight without a corresponding metric.[ 45] Hence although the bias alters the decision boundary of the Virtual Operative Assistant, it is left out of metric-wise assessment. Third, another limitation of linear machine learning algorithms is that a very high positive score in one metric may overcompensate for other smaller negative metrics thereby impacting the classification. The authors believe that the Virtual Operative Assistant's individualized metric feedback offers the necessary transparency for users to identify these overcompensation situations. The system also avoids these cases through the step-wise teaching methods where competency in all metrics is necessary for success rather than competency in just a few metrics. Although video feedback is useful, it can be challenging for future users of the framework to record a video illustrating expertise in a metric as there may be multiple different ways that expertise in a metric could be achieved. To deal with this issue we are developing a system that can record the entire simulation and automatically extract the time point(s) and event(s) that led the algorithm to classify the user performance as novice for a particular metric. This shortened clip would be presented to the user along with film footage of expert performance to allow for self-reflection and direct comparison between skilled and novice execution of that defined metric.

Social and ethical impacts of AI-based teaching

It is important to recognize the potential social and ethical impacts of integrating artificial intelligence into educational paradigms. Medicine, like other disciplines, is influenced by developing trends in other scientific areas. Algorithms will need to be updated to reflect both the evolution of present AI methodologies and development of new medical technologies. The optimal frequency of algorithm updating is unknown. Frequent updating may allow insignificant time to verify an algorism's teaching potential and limitations, while rare updates may cause the algorithm's perception of expertise to fall behind the evolving definition.

Another important consideration of AI-powered teaching is whether the platform allows users to train to become more skilled or simply lays out a path for users to understand how to "cheat" the algorithm and give the illusion of being skilled. It is important to recognize that AI-based teaching platforms should undergo rigorous validation through experts' opinion and multi-institutional studies to assess transferability of expertise to real life scenarios.

AI-based teaching platforms suffer from their inability to replicate the affective component of feedback. These systems struggle to understand the current emotional and cognitive state of the learner and cannot always tailor feedback in a contextually appropriate manner. Research indicates that feedback can be ineffective or occasionally detrimental to skill acquisition if the learner feels disconnected from the instructor or the feedback is not disseminated appropriately.[ 46] While automated feedback may provide more objective and standardized training, human-to-human connection is also an important component of educational paradigms. AI-based teaching platforms should be integrated into multifactorial educational systems that are carefully constructed and evaluated utilizing a best practices approach. These methodologies should be incorporated with the optimal participation of human instructors in the learning process.

Conclusions and future directions

The Virtual Operative Assistant outlined in this study establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical and medical educational teaching. We demonstrate the ability of machine learning algorithms to not only different levels of expertise, but also identify teachable metrics which contribute to this classification. Our initial validation with a linear SVM revealed the feasibility of using the framework to build an automated and objective feedback platform for a complex neurosurgical task. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.

Future work will aim to employ an explainable approach with other algorithms including artificial neural networks and deep learning to assess surgical performance. Recent studies by our group have shown the ability of artificial neural networks to differentiate 3 groups of expertise level performing a spine surgery procedure in virtual reality.[ 5] As such we aim to adapt our framework to be able to provide feedback to more than 2 groups of expertise. We are also performing trials to determine both the optimal method of presenting the complex information derived from the Virtual Operative Assistant to the trainee, and to determine if expert instructors working with the Virtual Operative Assistant improves performance. We have developed a single-blinded 2 arm randomized controlled trial employing an AI focused surgical training curriculum in which half the surgical trainees will be trained, and the other half will act as controls. Their performance will be assessed in live operative settings to obtain predictive validation of the AI systems utilized. These studies will help establish the future role of AI assisted learning in surgical education.

Supporting information

S1 Video. Demonstration of the virtual reality subpial simulation scenario.

The participant holds a simulated ultrasonic aspirator in their dominant hand and a simulated bipolar in their non-dominant hand. The ultrasonic aspirator is used to remove the simulated tumor (yellow region) while the bipolar can be used to hold surrounding tissue (white regions) or coagulate bleeding.
https://doi.org/10.1371/journal.pone.0229596.s004
(MOV)

Acknowledgments

We thank all the neurosurgical residents, fellows and surgeons who participated in this study. We thank the McGill Division of Orthopaedic Surgery and the Montreal Neurological Institute for their internal support. We acknowledge Dr. Abdulrahman Sabbagh and thank Dr. Robert DiRaddo, group leader of Simulation, Life Sciences Division, National Research Council of Canada in Boucherville in developing the virtual reality scenario. We also thank Drs. Khalid Bajunaid, Abdulgadir Bugdadi and Ghusn Al Sideiri along with Robin Sawaya and Mina Khan for their help with the study. We acknowledge the support of Drs. Mahmoud Al-Yamany and Lahbib Soualmi from the National Neuroscience Institute, Department of Neurosurgery, King Fahad Medical City in Riyadh, and Dr. Saleh Baeesa from King Abdulaziz University, in Jeddah, Saudi Arabia.

References

21. Sawaya R, Bugdadi A, Azarnoush H, Winkler-Schwartz A, Alotaibi FE, Bajunaid K, et al. Virtual Reality Tumor Resection: The Force Pyramid Approach. Operative Neurosurgery. 2018;14(6):686-96. https://dx.doi.org/10.1093/ons/opx189 pmid:28962033.

# data[].attributes.attachment_link.title
The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

# data[].attributes.attachment_link.description
Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms' decision-making processes.

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1. Introduction

1.1. Mental Disorders

Mental disorders with onset in childhood or adolescence, as encapsulated by the Diagnostic and Statistical Manual of Mental Disorders DSM-V, include both neurodevelopmental disorders (NDDs), such as intellectual disability; specific learning disabilities, such as dyslexia, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASDs); and mental health disorders (MHDs), such as depressive, anxiety, stress related, conduct and psychotic disorders. Globally, childhood-onset mental disorders are becoming more prevalent, with a rate of 10-20% [ 1]. NDDs are now recognised as a leading cause of morbidity in children, with a huge impact not only on the individual child, but also on their families and society [ 2]. There is also a possibility of the development of suicidal ideation in the aforementioned depressive [ 3], anxiety [ 4], stress-related [ 5], conduct [ 6] and psychotic disorders [ 4]. Moreover, there is frequent overlap between NDDs and MHDs. For example, between 30% and 50% of children with ADHD are reported to have a comorbid MHD, such as depression [ 7, 8], and over 70% of children with ASD are reported to have a comorbid MHD, most commonly including anxiety (40%) and ADHD (30-40%) [ 9, 10]. The complex web of comorbidities between NDDs and MHDs can contribute to the severity of learning disabilities [ 11] in individual children and can further reduce the quality of life. This complex comorbidity can be associated with poorer long-term prognosis and has implications for tailoring treatments for individual children [ 12].

Both genetics and environmental factors contribute to the development of MDs in children. A child's neurological functioning and mental health can be influenced by the family's socioeconomic status, wherein poverty and low family income can affect the education, health and self-esteem of children, adversely affecting their growth, development and societal involvement. Furthermore, a parent's own poor health, mental disorder, social isolation or housing insecurity can diminish his/her capability of effectively parenting the child by providing a supportive environment for growth and development, which is necessary to keep mental health problems at bay [ 13].

Thus, childhood onset MDs in general, and NDDs specifically, have a huge impact on individuals, families and society, and there is a pressing need to understand how best to recognise and treat these conditions. This systematic review has two aims. First, to provide an overview of how AI technologies have been applied to assist students with the most common and impactful NDDs, namely ADHD, dyslexia and ASDs. Second, to highlight the limitations of existing developed AI tools to enable recommendations to be made for future directions in AI development, so that personalised education for these students can be improved further.

1.1.1. ADHD

The NDD ADHD, with prevalence reported between 9% and 40% [ 14], has multiple aetiologies wherein combinations of environmental and genetic aspects play a role in contributing to the pathogenesis and its diverse phenotypes [ 15]. Non-genetic risk factors have been suggested to include brain injury, premature delivery, maternal alcohol and tobacco use during pregnancy, and contact with certain environmental agents, such as lead, during pregnancy or at a young age [ 16, 17, 18]. Not uncommonly, children meeting diagnostic criteria for ADHD have other comorbidities, including oppositional defiant and conduct disorders (ODD and CDs) [ 19, 20] and depressive and anxiety disorders [ 21], as well as specific learning disorders [ 22]. Children with ADHD have difficulties in maintaining sustained attention, can be hyperactive, fidget and find it difficult to participate in turn taking [ 17]. Preliminary research points to differences in brain volumes in children diagnosed with ADHD compared to neurotypical children, particularly affecting the frontal and parietal cortices ( Figure 1).

1.1.2. Dyslexia

NDD dyslexia is a common type of learning disability, affecting 3 to 15% of school-age children [ 23]. Individuals with dyslexia have specific impairments in the development of expert reading skills. Dyslexia is characterised by difficulties with correct and/or fluent word recognition and poor spelling and decoding abilities [ 24]. Individuals with dyslexia have been shown to have differences in functional brain imaging compared to non-dyslexic individuals, for example, reduced neural adaptation to repetitive stimuli [ 25], as depicted in Figure 2. Children with dyslexia can have other specific learning deficits [ 24], low self-confidence, anxiety and depression.

1.1.3. Autism Spectrum Disorders

Autism spectrum disorders (ASDs), with a reported prevalence in developed countries of around 2% [ 26], typically present within the first three years of life. ASDs are characterised by challenges in social interaction [ 27, 28], speech and language delays, avoidance of eye contact, struggles to cope with changes in environment, the display of repetitive behaviours, and differences in learning profiles [ 26]. Children and adults with an ASD have a high frequency of anxiety and depression. Research into the pathophysiology of ASDs have revealed neurobiological differences between children with ASDs and those without ('neurotypical' children). Figure 3 highlights the excess neural connections between neurons in the brain of a child with an ASD compared to a neurotypical individual [ 29]. These excessive connections are thought to be secondary to reduced 'pruning' of damaged neuronal connections during brain development. This neuropathological difference is understood to result in disordered neural patterning across the brain and dysregulation in cognitive function coordination between different brain regions [ 30].

1.2. Personalised Assistive Tools Using Artificial Intelligence for Children with NDD

The prior summary highlights that NDDs, such as ASDs, ADHD and dyslexia, are globally prevalent conditions associated with poor learning outcomes and a high prevalence of comorbid MHDs. A recent study attests that more support strategies are required to help students with these conditions in their learning in mainstream schools [ 31]. There is a critical need to apply effective tools to improve learning outcomes. Personalised assistive educational tools might help improve educational outcomes, helping affected individuals to integrate better into society, reducing stigma, isolation and stressful events, such as bullying, that are known to be common triggers for suicide attempts [ 32]. Hence, Section 1 highlights the three main NDDs and the need for personalised assistive tools. Section 2 discusses instructional practices employed in schools, challenges encountered and the potential role of AI tools in addressing those challenges. Section 3 discusses the research methodology, while Section 4 provides a summary of AI tools developed and discusses the effectiveness of these tools for personalised education. In Section 5, the limitations of existing AI tools are discussed. In Section 6, the future AI tool that addresses the limitations of existing tools for personalised education is proposed. Chapter 7 concludes the review study.

2. Current Management Approaches for Children with NDD

2.1. Individualised Educational Approaches for a Child with ADHD

Inclusive approaches are recommended so that students with NDD can be included in mainstream schools wherever possible [ 33]. However, mainstream classroom settings can worsen symptoms in children with ADHD, especially when students are expected to sit still, remain quiet and stay focused [ 34, 35]. Mainstream schooling without additional support can thus damage children's self-esteem and negatively impact their relationships with teachers and peers [ 33]. Hence, to cater to the individual needs of these children, it is recommended that teachers employ individualised educational practices in the classroom [ 36, 37]. Details of such individualised educational practices are listed in Table 1.

2.2. Individualised Educational Approaches for Children with Dyslexia

A multi-sensory approach, whereby information is shown simultaneously through varying channels, is reported to be preferable when teaching dyslexic children in schools [ 18]. Table 2 summarises strategies that have resulted in successful outcomes when employed in the school context for children with a diagnosis of dyslexia.

2.3. Teaching Support in Schools for Students with ASDs

Children with ASDs think, learn and behave differently from neurotypical children and differences in auditory processing, inspiration, emulation and organisation can hamper the learning success of children with ASD [ 38]. Hence, unique structured teaching strategies have been employed to cater to the different learning needs of ASD children, as listed in Table 3 [ 38].

2.4. Challenges in Implementing Individualised Learning Approaches in Schools

Despite evidence that individualised lessons are effective in helping children overcome their learning disabilities, teachers face several challenges in achieving this goal [ 40]. It takes a lot of time for teachers to provide differentiated instructions, ensure the child has understood them, and then achieve goals that have been set for individual students [ 40]. Many schools have shortages of appropriate learning resources. Therefore, an assistive learning tool that can be personalised for children's individual learning challenges and needs would be very helpful for teachers and a great support in helping students meet their individual goals. Although learning a new tool and teaching children to learn the assistive tool may be time-consuming initially, the benefits of using such tools to support individualised learning and improve overall clinical and educational outcomes are clear.

2.5. Use of Artificial Intelligence in Therapies and Supportive Education of Children with Mental Disorders

2.5.1. Conventional Methods Using AI

Machine learning forms a part of artificial intelligence (AI) wherein the model is able to do tasks automatically, without needing any human interference. The conventional machine learning models are hence trained by the input data fed to them, after which these models are able to predict outcomes with high accuracies. Deep learning is a subfield of machine learning in which large data is used to train these models, which can also predict outcomes with high accuracies. Both models are commonly used in the diagnosis of some neurological disorders, such as autism [ 41, 42], ADHD [ 43, 44] and depression [ 45, 46, 47], with high accuracies. The models are either fed with images obtained from computerised tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans or electroencephalogram (EEG) signals for the diagnosis of neurological disorders. Figure 4 shows the sequence of steps involved in training a machine learning model for diagnosis; after the input of signals or images, pre-processing takes place to clean the data, after which features are extracted and ranked thereafter, to obtain the most significant features, before they are finally classified into normal or abnormal classes. While the feature extraction and selection processes need to be done manually by the researcher for conventional models, these processes are done automatically in deep models.

2.5.2. Advanced Methods Using AI

Some frequently used deep models include the convolutional neural network (CNN) [ 48, 49, 50], long-short term memory model (LSTM) [ 51] and autoencoder [ 52], as depicted in Figure 5, Figure 6 and Figure 7, respectively. In CNN models, the input data is fed to the convolution layer. New feature maps are created in each successive layer wherein more robust features are extracted for forecasting. In the final fully connected layer, the data is classified [ 48]. The LSTM encompasses three main blocks of memory cells, input, forget and output gates, which are accountable for controlling the information stored, read and written on the cell, respectively, as data comes in [ 51]. The LSTM principally performs by remembering important information from preceding states and building upon them [ 51]. Encoders are arranged to form the deeper autoencoder model. Autoencoders perform by encoding unlabelled input data and rebuilding the data accurately thereafter. These models comprise the coding and decoding phases, wherein the same weights are used to encode the feature and rebuild the output [ 52].

2.5.3. Importance of AI in Therapies and Supportive Education

AI has been used to assist in social skills training in children with ASDs to recognise and respond to social cues. Belpaeme et al. [ 53] used sensory features, such as facial expression, body movements and voice recordings, as inputs to a machine learning model (implemented in a robot) to analyse autistic children's behaviour and engagement levels for therapy. These input features were then combined with target outputs, engagement markers in this case, to train the model. The study proved the potential for the robot to adapt to its interactant, hence influencing engagement in participants. In another study, Sanghvi et al. [ 54] used postural expressions, such as silhouette images of the upper body, as they played chess with a robot to analyse the engagement levels of autistic children. Another positive outcome was established through this study; the potential of integrating representative data (as described above) with an affect recognition model to act as a game mate for autistic children in the real world. In a different study, Kim et al. [ 55] used audio recordings as input features to analyse the emotional states of autistic children. These features were fed to the support vector machine model, integrated with a robot, to assess their social engagement as they played with the robots. This enhanced audio-based emotion forecast approach discusses the possibility of sustaining a more natural interaction between autistic children and the robot, hence allowing the robot to assess the engagement level of the children more accurately and modify its responses to maintain an interactive learning environment. Other researchers have explored different input features, such as facial expressions [ 56], body movements [ 57], bio signals [ 58] and vocalisations [ 39]. In a more recent study, Esteban et al. [ 59] explored input features, such as facial expressions, direction of look, body posture and voice tones, to a model within the NAO robot to assess the social engagement of autistic children. This study attests to the capability of robots to possess increased autonomy, so as to lighten the load of therapists.

Moving towards individualisation, Rudovic et al. [ 60] developed a personalised deep model, using coordinated video recordings of head and body movements, facial expressions and gestures, audio recordings and bio signals such as heart rate, electrodermal activity and body temperature, to assess the engagement of autistic children. The results reported that the model matched human experts, the prediction of affect and engagement with that of human experts, with an accuracy of about 60%, outperforming non-personalised machine learning solutions. In another study, a hybrid physical education teaching tool was developed, wherein speech recognition combined with artificial intelligence was used to construct a personalised voice interactive educational robot. The results showed that the robot was able to answer students' questions, achieving a recognition accuracy of more than 90% [ 60]. Hence, the aforementioned studies affirm that AI is a promising avenue to improve social interaction and supportive education in children with mental disorders.

3. Materials and Methods

The systematic review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines to analyse the most relevant studies on assistive tools developed to address learning disabilities using machine learning models. The search was conducted between the years 2011 and 2021. The relevant journal articles were searched through the IEEE, Google Scholar, PubMed, Science Direct and Springer Link scientific repositories, as seen in Table 4. The Boolean search strings such as "machine learning," "artificial intelligence tools," "Autism spectrum disorder," "Attention deficit hyperactivity disorder," "dyslexia," "students" and "learning" were used in various combinations. The relevant articles were gleaned from the various databases for this review, based on three primary processes from the PRISMA guidelines. First, a total of 20,926 articles were identified based on the Boolean search strings for autism spectrum disorder, attention deficit hyperactivity disorder and dyslexia. From the IEEE, Google scholar, PubMed, Science Direct and Springer Link repositories, 1, 19, 800, 0, 445 and 680 articles were retrieved, respectively, wherein most articles described the diagnostics/prediction/screening of ASD, ADHD, dyslexia or other neurodevelopmental disorders. Subsequently, articles were screened wherein duplicate and irrelevant articles were excluded based on the inclusion and exclusion criteria. The process culminated with the selection of the most relevant articles, which was set to 26. The flowchart detailing the processes involved in the retrieval of appropriate articles using PRISMA guidelines is shown in Figure 8.

The search was conducted between February to March in 2021. Studies were included if they met the following criteria:

They described the use of AI tools to help students with ADHD, Dyslexia and/or ASD in their learning:

They were published between the years 2011 and 2021.

They were published in a peer-reviewed journal.

They were published in English.

Studies were excluded if:

They described the use of AI tools to help students with other disorders apart from the NDDs ADHD, dyslexia and/or ASD.

The article was not published in English.

The article was not published in a peer-reviewed journal.

The article was published before 2011.

4. Results

4.1. Summary of Articles Collated

Table 5 presents the summary of articles that involve the use of AI tools (machine learning techniques) for teaching children with learning disabilities. Abstracts were first searched and then a full text review was conducted to ensure that the studies met the inclusion and exclusion criteria and that data could be extracted on the AI tool used, the features/model used for training, the type of technology and the learning area addressed ( Table 5). Although not commonly reported, the effectiveness of any AI tools on students' learning was also recorded.

4.2. Effectiveness of AI Tools for Personalised Education

Most AI tools that were used for learning, as shown in Table 5, have reported positive outcomes. For example, the 'Child activity sensing and training tool' for ADHD [ 69], which was tested in authentic situations, has been identified as having the potential to aid ADHD students in gaining attention in school settings, as well as to track their physical and physiological activities in real time [ 69]. Additionally, this tool has the potential to aid an ADHD student who has lost focus in his/her work. Furthermore, the 'Emotify' game developed for autistic students has obtained an accuracy of 72% in emotion recognition [ 83], which is a testament to its successful application. Additionally, the application has enabled participants to experience more engagement and exhibit higher behavioural intentions towards it. A study that used 'Facesay' games to aid autistic students [ 86] reported improvements in emotion recognition, social interaction, facial recognition, emotion recognition and social interaction in low-functioning and high-functioning autistic students. The application has also been found to be very promising, cost-effective and efficient for teaching effect recognition and mentalising constructs to high-functioning ASD students. The development of software games to help students with challenging behaviour of ADHD students has resulted in positive impacts [ 85] in addressing their moods, wherein a reduction in challenging behaviours of participants was reported when the games were used as an intervention to improve behaviours. Apart from the applications, the use of robots [ 84, 85, 92, 93, 102, 103] and alternative communication devices [ 78, 86] have been proven to be effective in improving focus, as well as math and social skills and in the teaching of ASD students, respectively. Hence, the abovementioned findings confirm the effectiveness of AI tools for personalised education.

5. Discussion of Main Findings and Results of Study

Figure 9, derived from Table 5 illustrates the distribution of assistive tools used to aid ADHD, dyslexia and ASD students in their learning. Figure 10, also derived from Figure 4, depicts the various assistive tools used to aid ADHD, dyslexia and ASD students in their learning. Hence, based on Figure 9 and Figure 10, it has been established through this study that most assistive tools have been developed to support ASD students in their learning and that the most prevalently developed tools to help ADHD, dyslexia and ASD students in their learning are application-based tools. Through this review, it has also been well-established that robots and application-based tools have been developed most to support ASD students, as compared to ADHD and dyslexic students, in their learning. This is in line with an interview conducted with educators in England on humanised robots discussing optimistic views shared by some educators [ 69]. These educators highlighted specific cases where robots were bound to be impactful, for example as "stepping stones" to social interactions and the capabilities of personalised robots to meet the individual learning needs of learners with ASDs. Furthermore, educators also felt assured that the predictability and constancy of behaviour in robots would benefit learners with ASD, facilitate their learning and diminish the burdens on them [ 69]. The results of our review also confirm that certain AI tools have shown positive outcomes and have been successful in certain educational settings. From our review study ( Table 5), it is evident that mostly application-based tools and robots have been developed to aid students with an ASD in various aspects of their learning, from developing social and communication skills to daily living skills. Furthermore, it is noticeable that more types of technology have been used and hence more AI tools have been developed to aid students with ASD as compared to other learning disabilities ( Figure 10). Perhaps this may be due to the challenges in learning, social communication and play skills that individuals with ASDs face. Additionally, from the results of our review ( Table 5) and extant literature, it is evident that deep learning models are used to develop robots to learn a large variety of data due to the highly heterogenous nature of ASDs [ 105]. Similarly, application-based tools have also been used to address the learning needs of students with dyslexia. These tools possess special features that allow individualised learning for each student. A combination of wearable devices, robots and application-based tools have been developed to aid students with ADHD. However, from the results of our review ( Table 5), it has been established that not all developed tools possess special features that allow personalisation. The learning needs of a student depend on the severity and precise nature of how that disorder affects them, and thus varies from one student to another. Hence, we deduce that along with the development of AI-based assistive tools, it is imperative that special features are entrenched that allow personalisation, such that the distinctive learning need(s) of every student is met [ 30]. Despite its advantages, using AI to develop such tools also displays some limitations, as discussed below.

5.1. Limitations of Existing AI Tools for Personalised Education

5.1.1. Suitable Datasets

There is a scarcity of public databases because it is challenging to obtain data from children with certain NDDs because they frequently have difficulties in staying still. There is also limited data available that focus on the severity of such disorders. Many children have complex comorbidities, so developing personalised AI tools for such students can be challenging.

5.1.2. Ethical Considerations

There are ethical concerns about using AI-driven applications in the development of personalised tools for learning. Educational assistive technologies are commonly used with students who are adolescents; hence, ethical concerns arise over privacy, data security and informed consent, which need to be mitigated [ 106]. For example, the information collected about a student should be reduced to include only information necessary for the intended purpose [ 106], i.e., to train the machine learning model. Data collection of the student should start only once the individual knows that data collection is taking place and has consented to the data collection [ 106]. Most importantly, educators should also ensure that students understand the consequences of using assistive technologies to protect their privacy and data [ 107]. To ensure the consent is valid, the teacher could describe the potential risks and benefits in a way that does not prompt a specific decision from those individuals who may be affected by the use of the technologies [ 107]. Additionally, it is imperative that educational technology companies comply with the relevant regulations; for example, the Children's Online Privacy Protection Act [ 108] of the United States makes an attempt to determine the age of students who assess the company's applications, programmes or extensions for student data collection. If the student is below 13 years old, parental consent is required for data collection [ 107].

Thus, it is important that privacy considerations are respected and that student data used to train machine learning models is secure according to local ethics board requirements [ 109]. Safeguards need to be in place to ensure data cannot be hacked [ 109], and educators should ensure ethical considerations are in place before any student data is collected, when the assistive tool is being used.

5.1.3. Cost of Implementing AI Tools

Besides ethical issues, Tsai and Gasevic [ 110] corroborate other limitations of AI in the technical (system integration) [ 111] and financial aspects. The costs involved in installing, maintaining and repairing AI tools is another limitation of implementing these tools in classroom settings [ 112]. Hence, to make AI affordable and integrate it with ease, it is imperative for schools to assimilate AI technology in a cloud-based intranet [ 112]. This would be more cost-effective, as schools would just need to pay an affordable subscription cost monthly, wherein various aspects, such as the installation, storage space, specifications, maintenance and technical support would be taken care of [ 113].

5.1.4. Information Loss

Another limitation is the possible loss of pertinent information when AI tools require repair [ 102]. Information such as students' learning data may be compromised, hampering the training of AI models or assessment of existing tools. This could be averted by adopting a cloud-based approach to safeguard information [ 112].

5.2. Cloud Computing in Schools

In recent years, cloud computing is progressively becoming popular in delivering technology in the education domain [ 114]. Cloud computing also allows schools to use less potent computers to assess the cloud, hence reducing computing costs [ 114]. However, cloud computing has potential disadvantages, and privacy, security and legal (policies) issues need to be addressed to successfully implement a cloud-based system [ 114].

5.2.1. Advantages of Using AI in Cloud Computing

The processing of information and resources, comprising the storage, sharing, backup and recovery of substantial information and resources, is a crucial aspect of AI-based technology [ 115]. Hence, integrating AI technology in cloud computing is beneficial as the internet and central remote servers are used to sustain information, resources and applications, resulting in effective computing as storage, processing, memory and bandwidth are centralised [ 115]. Similarly, backup of information can be done effortlessly by cloud-based applications, unlike traditional methods of using hard discs that are time-consuming, expensive, contain limited capacity and can require laborious maintenance [ 116].

Merging the AI tool in the cloud is beneficial as it is cost-saving, wherein costs related to hardware and maintenance or on-site data centres are eliminated [ 117]. The AI tool is also able to computerise intricate and repetitive tasks to enhance productivity and perform data analysis using intelligent automation; hence, it does not require any human intervention. The AI-based tool is also able to perform data analysis rapidly and provide deeper insights and hence is competent in offering real-time personalised recommendations to users [ 117]. AI tools are able to simplify how data is used, modified and handled and thus improve data management when imbedded in cloud systems. AI-powered network security tools need to be used to improve security to protect critical data when imbedded in cloud systems. Using the cloud server is also beneficial as it is part of the internet of things (IoT), where the data on shared networks [ 118] enables faster and more accurate outcomes. It also optimises equipment and usage of resources [ 118] that aid in managing situations better, such as being able to develop personalised tools for individuals. The core advantages of using cloud computing in school settings for personalised education are summarised in Figure 11, as seen below.

5.2.2. Disadvantages of Using AI in Cloud Computing

While AI in cloud computing may generally lower costs, a well-trained staff needs to be employed to operate the complex AI system and this may incur some cost. The cloud system would also require continuous internet connection to work well; weak internet access can impair the benefits of the cloud system [ 117]. Additionally, since data processing is speedier in the cloud than without using the cloud (methods discussed in Table 5), a time lag between transferring data to the cloud and receiving responses may be present. Privacy policies need to be complied with for data security when AI is used in cloud computing [ 117].

Based on the discussion above, it is apparent that there are downfalls to using AI in cloud computing. For instance, as mentioned earlier, data safety and security are the major concerns of using AI tools/AI in cloud computing. To tackle cyber security issues, decentralisation should be used wherein information acquired is split into parts and stored in various parts of the network instead of storing the entire information in a central server [ 119]. Additionally, user privacy methods, such as including a physical layer that conceals certain measurements of users [ 120] or enhanced security keys that create provisional identifications [ 121], should be employed within the cloud system to tackle privacy issues. Ethical considerations should also be followed. Despite the limitations, it can be observed that the benefits of integrating the proposed AI tool in the cloud outweigh the limitations, especially in the development of personalised tools.

6. Proposal for a Future AI Tool

Presently, machine learning techniques have not been explored in cloud-based applications for personalised learning tools. Hence, in our future work, we will be using deep learning techniques to develop a unique cloud-based model or an application-based tool that enables personalisation and serves as an assistive tool for teachers or other adults who assist in learning. Sizeable data comprising input features, such as facial expression images, speech signals, bio signals and clinical information, such as age, gender, genetic history, and so on would be used to develop the personalised model. The data obtained from the individual user will be sent to the trained deep learning model, which will be kept in the cloud server. The model will then be able to predict the user's learning needs accordingly and provide personalised learning to suit the child's learning needs. The proposed tool will also have decentralisation and user privacy methods implemented in it. The proposed AI-based tool for personalised learning is shown in Figure 12.

7. Conclusions

This review highlights that despite being in its infancy stage, assisted tools have been proposed to address the learning needs and quality of life of children with the most common NDDs: ADHD, dyslexia and ASDs [ 122]. The majority of the existing works using AI have focused on ASD. It is clear that more work needs to be done on the development and evaluation of assistive technologies for children with a range of NDDs [ 62]. The studies done to date have shown that AI-assisted tools have shown positive impacts on student's learning, and have been found to be acceptable by teachers, parents, special educators and therapists and feasible to implement in their teaching or therapeutic practices [ 122]. AI techniques have been reported to assimilate the independence of user's actions and enable children with learning difficulties to achieve their individual learning goals [ 122]. However, as discussed previously, our review reports that the existing AI assistive tools exhibit some limitations, so more work still needs to be done to 'mainstream' such approaches and maximise their impact. For example, the AI tools reported here are not embedded in cloud systems, hence limiting their ability to provide real-time suggestions for personalised learning. AI-based tools in the cloud system, such as digital applications, could be a major advance facilitating the provision of personalised specialist education and learning for affected individuals in real time.

Author Contributions

Conceptualisation, P.D.B. and U.R.A.; methodology, E.P.; formal analysis, S.L.O. and R.G.; writing-original draft preparation, J.V.; writing-review and editing, J.V., E.P., N.A.K. and M.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Informed Consent Statement

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

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Reading comprehension

Establish a sustained silent reading time daily.
Allow the child to read a book silently while listening to the teacher reading the story to the whole class.
Getting the child to make a storyboard, retell a story during story sessions, role-play characters in a favourite story.
Allowing the child to play board games/computer games to enhance reading comprehension skills.
Maintaining a word-bank book for words that are hard to read.
Providing students with another set of books to be read at home [ 36]

Phonics

Teaching children simple reminders on how to learn tougher phonics.
Teaching children how to recognise word families for phonetic concepts.
Allowing students to play board games, such as Bingo or computer games, to enhance phonics.
Using picture-letter charts for children who can identify sounds but not letters [ 36]

Writing

Using storyboards to teach students to recognise parts of a story for writing.
Creating a post-office in the classroom for students to write and receive letters from their teachers and peers.
Using tape recorders to dictate as an alternative to writing or having teacher/peer to write for students who would tell the story [ 36]

Spelling

Aligning spelling words to frequently used words by children everyday.
Partnering the child with another peer to encourage each other to quiz on spelling words.
Using colour-coded letters to help students spell difficult words.
Combining movement activities with spelling lessons [ 36]

Handwriting

Using special writing paper or teaching how to use a finger spacing to space out each word when writing.
Teaching handwriting skills through structured programmes [ 36]

Mathematics computation

Using mnemonics to describe fundamental steps easily for Maths computation.
Colour-coding arithmetic symbols to provide visual cues, allowing students to use calculators for basic computation, using computer/board games for practicing computations [ 36]

1. Failure in reading, grouping letters in words.

Using visual perceptions, such as signage or touching letters, to help in reading.
Providing simultaneous stimulation of each ear using different sounds [ 23]

4. Writing

Using the syntactic approach to teach punctuation and sentences/paragraphs.
Using the 'segmentation with highlighting' technique for sentence and text segmentation [ 23]

IEEE

"Autism spectrum disorder", AND/OR "Attention deficit hyperactivity disorder" AND/OR "dyslexia", Artificial intelligence AND/OR tools, students AND/OR learning

Machine learning, Neural networks, deep learning

Autism: 0, ADHD: 1, Dyslexia: 0

Google Scholar

Autism: 12 100, ADHD: 3900, Dyslexia: 3800

PubMed

Autism: 0, ADHD: 0, Dyslexia: 0

Science Direct

Autism: 176, ADHD: 172, Dyslexia: 97

Springer Link

Autism: 357, ADHD: 179, Dyslexia: 144

Author/Year

AI Tool

Features/Model Used for Training

Type of Technology

Learning Area Addressed

Effectiveness

AI Assistive Tools Used to Teach Students with ADHD.

Child activity sensing and training tool

Real time assistive technology

Real-time assistive tool that tracks activities and helps students sustain attention.

An assistive intervention that is based on a smartphone and has the potential to aid a child with ADHD who has lost focus in his/her work.

AI assistive tools used to teach students with Dyslexia.

Applications for reading and writing
(Learning Ally, Natural reader, dyslexia quest, sound literacy, ginger page, v books pdf voice reader, openWeb, reading intro by OZ phonics, OCR instantly pro, MindMeister)

Generation of audio files, pytorch deep convolutional text-to-speech models
(PytorchDcTts)

Digital application

Helps students with reading and writing skills.

Audiobooks, such as learning Ally, has enabled students to gain confidence, independence and success [ 72].

DIMMAND, capturaTalk application

Digital application

Provides tailored interventions for difficulties encountered in literacy.

Information is not available.

AI assistive tools used to teach students with ASD.

LIFEisGAME game

Digital application

To teach students to recognise facial emotions.

Information is not available.

ABA flashcards- Emotions, Autism emotion, conversation builder, emotions and feelings- autism, Find me, Kid in storybook maker, learning with Rufus, Look in my eyes: Train engineer, Model me going places 2, Pictello, Social stories, Special stories, The social express, Toca Boca

-

Deep learning/machine learning algorithms using unstructured data

Digital application

Teaches social skills

Information is not available.

ABA find it, Agnitus, Autism learning games- camp discovery, Intro to letters, Intro to Math, Math Bingo, Pop Math, Starfall ABC, Word wagon

-

Deep learning/machine learning algorithms using unstructured data

Digital application

Helps in different learning areas

The camp discovery enabled participants to show high learning rates over a short period of time. It has been suggested that the application teaches the selected skills effectively [ 89].

Milo, NAO, Pepper, Aisoy 1, Keepon robots

-

Supervised machine learning algorithms; generalised models trained on users' data and individualised models trained on initial subclass of users' data [ 94]

Assistive technology

Helps build social and communication skills.

Social robots, such as NAO, have been reported to improve social skills in students, especially in terms of eye contact and concentration. Nonverbal children also reportedly started pronouncing some words [ 95].

Facesay games

Digital application

Software games help recognise behavioural and emotional clues and enhance social skills.

Facesay application is found to be very promising, cost-effective and efficient for teaching affect recognition and mentalising constructs to high-functioning ASD students [ 101].

Life skills winner application

-

Deep learning/machine learning algorithms using unstructured data

Digital application

Teaches students daily living skills through the application.

Information is not available.

Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Barua PD, Vicnesh J, Gururajan R, Oh SL, Palmer E, Azizan MM, Kadri NA, Acharya UR. Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders-A Review. International Journal of Environmental Research and Public Health. 2022; 19(3):1192. https://doi.org/10.3390/ijerph19031192

Chicago/Turabian Style

Barua, Prabal Datta, Jahmunah Vicnesh, Raj Gururajan, Shu Lih Oh, Elizabeth Palmer, Muhammad Mokhzaini Azizan, Nahrizul Adib Kadri, and U. Rajendra Acharya. 2022. "Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders-A Review" International Journal of Environmental Research and Public Health 19, no. 3: 1192. https://doi.org/10.3390/ijerph19031192

# data[].attributes.attachment_link.title
Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders-A Review

# data[].attributes.attachment_link.description
Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders.

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26 Pages Posted: 7 Dec 2020

Date Written: December 2020

Abstract

Across the world, Artificial Intelligence (AI) applications are entering all domains of our lives, including the educational environment. This development was further spurred by the COVID-19 pandemic, which rendered many educational institutions dependent on (AI-enabled) digital learning tools to continue their activities. While the use of AI systems can lead to numerous benefits, it can also entail ethical risks, which are increasingly appearing on legislators' agendas. Many of these risks are context-specific and increase in significance when vulnerable individuals are involved, asymmetries of power exist, or human rights and democratic values are at stake more generally. Surprisingly, however, regulators have thus far paid only little attention to the specific risks arising in the context of Artificial Intelligence in education (AIED). This paper hence aims to assess the ethical challenges posed by AIED. Its normative framework consists of the seven requirements for Trustworthy AI, as set out in the Ethics Guidelines of the European Commission's High-Level Expert Group on AI. After an overview of the broader context in which these requirements took shape (Section 2), the paper examines each requirement in the educational realm, as well as the pitfalls that should be addressed to enable their realisation (Section 3). Particular attention is given to the special role of education in shaping people's minds, and the manner in which this role can be used both to empower and exploit individuals. The paper notes that AIED's main strengths - offering education on a wider scale, through more flexible and individualised learning methods, and through the closer monitoring of students' reception of the materials - are also its main liabilities when left unchecked. Finally, the paper discusses various pathways that policymakers should consider to foster Trustworthy AIED beyond the adoption of guidelines (Section 4), before concluding (Section 5).

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trustworthy artificial intelligence in education: pitfalls and pathways

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Trustworthy Artificial Intelligence in Education: Pitfalls and Pathways

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Across the world, Artificial Intelligence (AI) applications are entering all domains of our lives, including the educational environment. This development was f

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Open Research: The Effective and Ethical Development of Artificial Intelligence: An Opportunity to Improve Our Wellbeing

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Applications of Artificial Intelligence in Business, Education and Healthcare

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This book discusses the role of artificial intelligence in the current COVID-19 pandemic, in the health sector, education and others

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Role of artificial intelligence in operations environment: a review and bibliometric analysis

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Role of artificial intelligence in operations environment: a review and bibliometric analysis - Author: Pavitra Dhamija, Surajit Bag

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Pandemic-accelerated Digital Transformation of a Born Digital Higher Education Institution on JSTOR

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Albert Rof, Andrea Bikfalvi, Pilar Marques, Pandemic-accelerated Digital Transformation of a Born Digital Higher Education Institution, Educational Technology & Society, Vol. 25, No. 1 (January 2022), pp. 124-141

# data[].attributes.attachment_link.readable_text
Abstract

Gathering data is essential to the research of education. Tradition approaches to gather data include statistics, survey and questionnaire, which spends much time but difficult to represent the real world with comprehensiveness, promptness and accuracy, having a negative impact on the reliability and validity. However, by using the Artificial Intelligence (AI), the big data system for gathering and cleaning data improves the accuracy, integrity, consistency, validity, uniqueness and stability of research, resulting in a higher reliability and easier analysis for the data. Through the big data system, the researchers can get the data with convenience, which promote a new methodology and help achieve the goal of education research.

Keywords

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References

Buchanan, J., Chai, D.H., Deakin, S.: Empirical analysis of legal institutions and institutional change: multiple-methods approaches and their application to corporate governance research. J. Inst. Econ. 10(1), 1-20 (2014)

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Author information

Authors and Affiliations

1. Research Institute of Information Technology Tsinghua University, Beijing, China

Chunmei Huang, Dun Chen & Wei Guo

2. Beijing Technology and Business University, Beijing, China

Chunmei Huang, Dun Chen & Wei Guo

3. People's Public, Security University of China, Beijing, China

Chunmei Huang, Dun Chen & Wei Guo

Corresponding author

Correspondence to Dun Chen .

Editor information

Editors and Affiliations

1. Xiamen University, Xiamen, China

Wenxing Hong

2. Tsinghua University, Beijing, China

Chao Li

3. Tianjin University, Tianjin, China

Qing Wang

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Huang, C., Chen, D., Guo, W. (2020). Innovation in Methodology of Education: Big Data and Artificial Intelligence. In: Hong, W., Li, C., Wang, Q. (eds) Technology-Inspired Smart Learning for Future Education. NCCSTE 2019. Communications in Computer and Information Science, vol 1216. Springer, Singapore. https://doi.org/10.1007/978-981-15-5390-5_5

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Innovation in Methodology of Education: Big Data and Artificial Intelligence

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Gathering data is essential to the research of education. Tradition approaches to gather data include statistics, survey and questionnaire, which spends much time but difficult to represent the real world with comprehensiveness, promptness and accuracy, having a...

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Abstract

Purpose

The last few years have seen a surge of interest in artificial intelligence (AI). The purpose of this paper is to capture a snapshot of perceptions of the potential impact of AI on academic libraries and to reflect on its implications for library work.

Design/methodology/approach

The data for the study were interviews with 33 library directors, library commentators and experts in education and publishing.

Findings

Interviewees identified impacts of AI on search and resource discovery, on scholarly publishing and on learning. Challenges included libraries being left outside the focus of development, ethical concerns, intelligibility of decisions and data quality. Some threat to jobs was perceived. A number of potential roles for academic libraries were identified such as data acquisition and curation, AI tool acquisition and infrastructure building, aiding user navigation and data literacy.

Originality/value

This is one of the first papers to examine current expectations around the impact of AI on academic libraries. The authors propose the paradigm of the intelligent library to capture the potential impact of AI for libraries.

Citation

Cox, A.M., Pinfield, S. and Rutter, S. (2019), "The intelligent library: Thought leaders' views on the likely impact of artificial intelligence on academic libraries", Library Hi Tech, Vol. 37 No. 3, pp. 418-435. https://doi.org/10.1108/LHT-08-2018-0105

Publisher
:
Emerald Publishing Limited

Copyright

# data[].attributes.attachment_link.title
The intelligent library: Thought leaders' views on the likely impact of artificial intelligence on academic libraries

# data[].attributes.attachment_link.description
The intelligent library: Thought leaders' views on the likely impact of artificial intelligence on academic libraries - Author: Andrew M. Cox, Stephen Pinfield, Sophie Rutter

# data[].attributes.attachment_link.title
Digital transformation as the main condition for the development of modern higher education | Educational Technology Quarterly

# data[].attributes.attachment_link.description
The article considers the potential and limitations that arise in the higher education system as a result of the e-technology use. E-learning has become an integral part of the 21st century education system. The pace of dissemination and implementation of e-technologies in all spheres of public life…

# data[].attributes.attachment_link.title
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence

# data[].attributes.attachment_link.description
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin.

# data[].attributes.attachment_link.readable_text
The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation

Abstract

In July 2017, China's State Council released the country's strategy for developing artificial intelligence (AI), entitled 'New Generation Artificial Intelligence Development Plan' (新一代人工智能发展规划). This strategy outlined China's aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan (ca. 150 billion dollars) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China's AI policies or have assessed the country's technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China's AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China's AI policy by bringing together debates and analyses of a wide array of policy documents.

1 Introduction

In March 2016, a Google DeepMind artificial intelligence (AI) designed for playing the board game Go (AlphaGo) defeated Lee Sedol, a South Korean professional Go player. At the time, Sedol had the second-highest number of Go international championship victories, yet lost against AlphaGo by four games to one (Boroweic 2016). While the match received some coverage in the West, it was a major event in China, where over 280 million people watched it live. Two government insiders described this match as a 'Sputnik moment' for the development of AI within China (Lee 2018, p. 3). Although there had been AI policy initiatives in the country previously, the victory for AlphaGo contributed to an increase in focus, as indicated by the 2017 'New Generation Artificial Intelligence Development Plan' (AIDP). The AIDP set out strategic aims and delineated the overarching goal of making China the world leader in AI by 2030.1

To provide this contextualised understanding, Sect. 2 maps relevant AI legislation in China. We argue that, although previous policy initiatives have stated an intent to develop AI, these efforts have been fractious and viewed AI as one of many tools in achieving a different set goal. In contrast, the AIDP is the first national-level legislative effort that focuses explicitly on the development of AI as a unified strategy. Following this, Sect. 3 analyses the interventions and impact of the AIDP on three strategic areas identified in the document, namely: international competition, economic growth, and social governance. Section 4 focuses on China's aim to develop ethical norms and standards for AI. There we argue that, although the debate is in its early stages, the desire to define normative boundaries for acceptable uses of AI is present and pressing. Altogether, this article seeks to provide a detailed and critical understanding of the reasons behind, and the current trajectory of, China's AI strategy. It emphasises that the Chinese government is aware of the potential benefits, practical risks, and the ethical challenges that AI presents, and that the direction of China's AI strategy will largely be determined by the interplay of these factors and by the extent to which government's interests may outweigh ethical concerns. Section 5 concludes the paper by summarising the key findings of our analysis.

2 AI legislation in China

Since 2013, China has published several national-level policy documents, which reflect the intention to develop and deploy AI in a variety of sectors. For example, in 2015, the State Council released guidelines on China's 'Internet +' action. It sought to integrate the internet into all elements of the economy and society. The document clearly stated the importance of cultivating emerging AI industries and investing in research and development. In the same year, the 10-year plan 'Made in China 2025' was released, with the aim to transform China into the dominant player in global high-tech manufacturing, including AI (McBride and Chatzky 2019). Another notable example is the Central Committee of the Communist Party of China's (CCP) 13th 5-year plan,2 published in March 2016. The document mentioned AI as one of the six critical areas for developing the country's emerging industries (CCP 2016), and as an important factor in stimulating economic growth. When read together, these documents indicate that there has been a conscious effort to develop and use AI in China for some time, even before 'the Sputnik moment'. However, prior to 2016, AI was presented merely as one technology among many others, which could be useful in achieving a range of policy goals. This changed with the release of the AIDP.

2.1 The New generation artificial intelligence development plan (AIDP)

Released in July 2017 by the State Council (which is the chief administrative body within China), the 'New Generation Artificial Intelligence Development Plan' (AIDP) acts as a unified document that outlines China's AI policy objectives. Chinese media have referred to it as 'year one of China's AI development strategy' ("China AI Development Report" 2018, p. 63). The overarching aim of the policy, as articulated by the AIDP, is to make China the world centre of AI innovation by 2030, and make AI 'the main driving force for China's industrial upgrading and economic transformation' (AIDP 2017). The AIDP also indicates the importance of using AI in a broader range of sectors, including defence and social welfare, and focuses on the need to develop standards and ethical norms for the use of AI. Altogether, the Plan provides a comprehensive AI strategy and challenges other leading powers in many key areas.

The AIDP delineates three key steps, each of which contains a series of goals, some of which are tightly defined, while others are vaguer. They are summarised as follows and in Fig. 1 below:

1. 1.
By 2020, China aims to maintain competitiveness with other major powers and optimise its AI development environment. In monetary terms, China intends to create an AI industry worth more than 150 billion yuan (ca. 21 billion dollars). Lastly, it seeks to establish initial ethical norms, policies, and regulations for vital areas of AI.

2. 2.
By 2025, China aims to have achieved a 'major breakthrough' (as stated in the document) in basic AI theory and to be world-leading in some applications ('some technologies and applications achieve a world-leading level'). China also targets an increase in the worth of its core AI industry to over 400 billion yuan (ca. 58 billion dollars), and plans to expand upon, and codify in law, ethical standards for AI.

3. 3.
By 2030, China seeks to become the world's innovation centre for AI. By then, growth in the core AI industry is expected to more than double again and be valued at 1 trillion yuan (ca 147 billion dollars), and further upgrades in the laws and standards are also to be expected, to deal with newly emerging challenges.

2.2 Implementing the AIDP

The Plan will be guided by a new AI Strategy Advisory Committee, established in November 2017, and will be coordinated by the Ministry of Science and Technology (MIST), alongside the AI Plan Promotion Office, and other relevant bodies ("AI Policy-China" n.d.).3 Although these bodies will provide central guidance, the Plan is not meant to act as a centrally enacted initiative. The AIDP instead functions as a stamp of approval for de-risking and actively incentivising local projects that make use of AI. Recognising this point is important: the AIDP is an ambitious strategy set by the central government, but the actual innovation and transformation is expected to be driven by the private sector and local governments. In other words, it is more appropriate to view the AIDP as a highly incentivised 'wish list', to nudge, and coordinate other relevant stakeholders, rather than a central directive (Sheehan 2018). This is why the 3-year plan promoting the AIDP (2018-2020) emphasises coordination between provinces and local governments.

With regard to the private sector, China has selected 'AI national champions': businesses endorsed by the government to focus on developing specific sectors of AI. For example, Baidu has been tasked with the development of autonomous driving, Alibaba with the development of smart cities, and Tencent with computer vision for medical diagnoses (Jing and Dai 2017). Being endorsed as a national champion involves a deal whereby private companies agree to focus on the government's strategic aims. In return, these companies receive preferential contract bidding, easier access to finance, and sometimes market share protection. Although other companies can compete in these fields, historically the status of 'national champion' has helped larger companies dominate their respective sectors (Graceffo 2017).

With this said, the new AI 'national team' differs from previous state-sponsored national champions in that they are already internationally successful in their respective fields, independently of this preferential treatment. Furthermore, there is extensive domestic competition in the areas where national champions have been selected. This suggests that competition may not be stymied in the traditional manner. For instance, all the companies selected as AI national champions are developing technologies in Alibaba's designated area of smart cities (Ding 2019). In parallel with this, patronage does not prohibit smaller companies benefiting from the financial incentive structure. Technology start-ups within China often receive government support and subsidies for developing AI technologies. As an example, Zhongguancun Innovation Town is a purpose-built, government subsidised, incubator workspace that provides a suite of services to help Chinese technology start-ups succeed, often in the sectors where national champions have been selected. Finally, there are also cases where there is no specific endorsement. For example, while the AIDP promotes smart courts, with a stated desire to develop AI for evidence collection, case analysis, and legal document reading, as of April 2020 there is no national champion selected for developing AI applications for the administration of justice.

These incentive structures contain a subtle distinction. A national team has been selected to lead the research and development in a handful of designated strategic areas. Beyond these selected companies, there are few specific guidelines provided to industry and local state agents as to which items to pursue on the AIDP's 'wish list'. This enables companies to cherry-pick the technologies they want to develop and provides local governments with a choice of private sector partners for integrating AI into city infrastructure or governance (Sheehan 2018). Subsequent documentation has emphasised the importance of strengthening organisation and implementation,4 including between provinces and ministries, yet it is unclear how this coordination would function in practice. Thus, the AIDP may work as a 'wish list', but the exact guidance, incentivisation and risk differ depending on the type of stakeholder.

The AIDP should not be read in isolation when considering China's AI strategy (Ding 2018), but it does provide the most transparent and influential indication of the driving forces behind China's AI strategy. Because of the AIDP's significance (in terms of policy) and importance (in terms of strategy), in the rest of this article, we shall use it as the organisational skeleton for explaining the drivers and ethical boundaries shaping China's approach to AI.

3 China's AI strategic focus

The AIDP provides a longitudinal perspective on China's strategic situation regarding AI, including its comparative capabilities, the opportunities offered, and the potential risks. Following a technology-first approach, it may be tempting to concentrate one's attention on the stated capabilities of AI, to gain an insight into the types of technologies in which China is investing. However, this would likely offer only a short-term perspective and would soon be out of date as technological innovation advances rapidly. Furthermore, it would do little to explain why China is seeking to develop a strong AI sector in the decades to come. To this end, it is more useful to try to understand China's strategic focus from a policy-first approach, by analysing the areas where China considers that AI presents opportunities. In this section, we focus on these areas of particular importance to China, on how and what China expects to gain from developing AI in each of them, and on some of the perceived risks present in each of these areas. The AIDP highlights three areas where AI can make a substantial difference within China: international competition, economic development, and social governance. They are strictly interrelated but, for the sake of clarity, we shall analyse them separately, and contextualise each of them by discussing the relevant literature surrounding the broader political backdrop and contemporary policy debates.

3.1 International competition

The AIDP states that AI has become a new focus of international competition and that 'the development of AI [is] [...] a major strategy to enhance national competitiveness and protect national security' (AIDP 2017). It emphasises that China should take the strategic opportunity afforded by AI to make 'leapfrog developments'5 in military capabilities. Although China and the US are regularly portrayed as geopolitical rivals (Mearsheimer 2010; Zhao 2015), the military budgets of the two powers remain significantly different. China has the world's second-largest military budget, with $175 billion allocated in 2019 (Chan and Zhen 2019), but its spending is still only a third of the US budget (Martina and Blanchard 2019). Rather than outspending the US in conventional weaponry, China considers investing in AI as an opportunity to make radical breakthroughs in military technologies and thus overtake the US.

One area in which China has been developing these asymmetric tactics is cyber warfare, where capabilities have been developed for targeting the US military's battle-critical networks if needed (Kania 2017a). Alongside this, evidence points to the persistent use of cyberattacks to collect scientific, technological and commercial intelligence (Inkster 2010). The Chinese position on these capabilities is ambivalent. On the one hand, China has officially promoted international initiatives for regulating hostile state-run activities in cyberspace, and to fill the existing regulatory gap for state behaviour in this domain (Ku 2017; Austin 2016; Taddeo 2012; Taddeo 2016). For example, China co-sponsored the International Code of Conduct for Information Security at the UN General Assembly in September 2011, which sought a commitment against using information technologies in acts of aggression and has provided continued support for dialogue by the UN Group of Government Experts in preventing cyber conflicts (Meyer 2020). On the other hand, China has also run cyber operations targeting US infrastructure and aiming at extracting commercial and scientific information as well as acquiring relevant intelligence against several countries, including Australia, Philippines, Hong Kong, and the US.6

As China has developed economically and militarily, the focus of the country's military strategy has also matured. Over the past few years, China's strategy has coalesced around efforts to develop 'new concept weapons' to surpass the US's military capabilities. These are not limited to AI alone, and are applicable to China's investments in other fields of emerging military technologies, like hypersonic weaponry (Kania 2017b). Therefore, China's efforts to use technology to gain an advantage in military affairs should not be seen as something new, but instead understood within a broader historical context of finding innovative ways to challenge the hegemony of the US.

Although the push for leapfrog developments marks a continuation of previous policy, there are strong concurrent indications that Chinese officials are also concerned about AI causing an arms race and potential military escalation. Statements of senior officials seem to suggest a belief in cooperation and arms control to mitigate the risks that AI's military development poses. In particular, three major risks are central to the debate:

1. (i)
human involvement and control once AI-based weapons are deployed;

2. (ii)
the absence of well-defined norms for state behaviour and use of AI weapons, which in turn increases;

3. (iii)
the likelihood of misperceptions or unintentional conflict escalation (Taddeo and Floridi 2018; Allen 2019).

Taken together, China's focus on military AI can be considered as a continuation of a longer-term strategy, which privileges developing (with the threat of deploying) technology to gain a military advantage. There remains a conscious recognition, by several actors in China, that developing AI presents an especially fraught risk of igniting an arms race or causing unintentional escalation due to the autonomy of these technologies (Taddeo and Floridi 2018; Allen 2019). But at the political level, efforts to curtail the use of military AI internationally may also be seen as part of a propaganda strategy.

3.2 Economic development

Economic development is the second strategic opportunity explicitly mentioned in the AIDP. It is stated that AI will be the driving force behind a new round of industrial transformation, which will 'inject new kinetic energy into China's economic growth' (AIDP 2017). The reconstruction of economic activity is targeted in all sectors, with manufacturing, agriculture, logistics, and finance being the examples promoted in the AIDP.

Some commentators have argued that maintaining these high levels of growth is particularly important for China due to the implicit trading by citizens of political freedoms for economic growth and embourgeoisement (Balding 2019). Research has highlighted that support for the party and a relatively lackluster desire for democracy stems from satisfaction with employment and material aspects of life, particularly within the middle classes (Chen 2013). Slowing economic growth would likely sow dissatisfaction within the populace and make inherent features within the Chinese political system, such as corruption, less tolerable (Diamond 2003; Pei 2015). A lack of a democratic outlet for this frustration could lower the overall support that the government currently receives. Some maintain that this creates a 'democratise or die' dynamic (Huang 2013), however, this may be unfeasible, given China's political control (Dickson 2003; Chin 2018).

3.3 Social governance

Social governance, or more literally in Chinese 'social construction',7 is the third area in which AI is promoted as a strategic opportunity for China. Alongside an economic slowdown, China is facing emerging social challenges, hindering its pursuit of becoming a 'moderately prosperous society' (AIDP 2017). An aging population and constraints on the environment and other resources are explicit examples provided in the AIDP of the societal problems that China is facing. Thus, the AIDP outlines the goal of using AI within a variety of public services to make the governance of social services more precise and, in doing so, mitigate these challenges and improve people's lives.

China has experienced some of the most rapid structural changes of any country in the past 40 years. It has been shifting from a planned to a market economy and from a rural to an urban society (Naughton 2007). These changes have helped facilitate economic development, but also introduced a number of social issues. One of the most pressing social challenges China is facing is the absence of a well-established welfare system (Wong 2005). Under the planned economy, workers were guaranteed cradle-to-grave benefits, including employment security and welfare benefits, which were provided through local state enterprises or rural collectives (Selden and You 1997). China's move towards a socialist market economy since the 1990s has accelerated a shift of these provisions from enterprises and local collectives to state and societal agencies (Ringen and Ngok 2017). In practice, China has struggled to develop mature pension and health insurance programmes, creating gaps in the social safety net (Naughton 2007). Although several initiatives have been introduced to alleviate these issues (Li et al. 2013), the country has found it difficult to implement them (Ringen and Ngok 2017).

The centrality of these concerns could be seen at the 19 th National Party Congress in 2017, where President Xi declared that the 'principal contradiction' in China had changed. Although the previous 'contradiction' focused on 'the ever-growing material and cultural needs of the people and backward social production,' Xi stated 'what we now face is the contradiction between unbalanced and inadequate development and the people's ever-growing needs for a better life' (Meng 2017). After years of focusing on untempered economic growth, President Xi's remarks emphasise a broader shift in China's approach to dealing with the consequences of economic liberalisation.

These statements are mirrored in several government plans, including the State Council Initiative, 'Healthy China 2030', which seeks to overhaul the healthcare system. Similar trends can be seen in China's efforts to clean up its environment, with a new 3-year plan building on previous relevant initiatives (Leng 2018). China has recently focused on AI as a way of overcoming these problems and improving the welfare of citizens. It has been pointed out that China's major development strategies rely on solutions driven by big data (Heilmann 2017). For example, 'Healthy China 2030' explicitly stresses the importance of technology in achieving China's healthcare reform strategy, and emphasises a switch from treatment to prevention, with AI development as a means to achieve the goal (Ho 2018). This approach also shapes environmental protection, where President Xi has been promoting 'digital environmental protection' (数字环保) (Kostka and Zhang 2018). Within this, AI is being used to predict and mitigate air pollution levels (Knight 2015), and to improve waste management and sorting ("AI-powered waste management underway in China" 2019).

The administration of justice is another area where the Chinese government has been advancing using AI to improve social governance. Under Xi Jinping, there has been an explicit aim to professionalise the legal system, which suffers from a lack of transparency, issues of local protectionism, and interference in court cases by local officials (Finder 2015). A variety of reforms have been introduced in an attempt to curtail these practices including, transferring responsibility for the management of local courts from local to provincial governments, the creation of a database where judges can report attempts at interference by local politicians, and a case registration system that makes it more difficult for courts to reject complex or contentious cases (Li 2016).

3.4 Moral governance

Social governance/construction in China does not just encompass material and environmental features, but also the behaviour of citizens. Scholars have argued that the disruption of the Maoist period followed by an 'opening up' has created a moral vacuum within China (Yan 2009; Lazarus 2016). These concerns are echoed by the Chinese public, with Ipsos Mori finding that concerns over 'moral decline' in China were twice as high as the global average (Atkinson and Skinner 2019).8 This is something that has been recognised by the Chinese government, with high-level officials, including President Xi, forwarding the idea of a 'minimum moral standard' within society (He 2015). This goal is not limited to ensuring 'good' governance in the traditional sense; it extends to the regulating the behaviour of citizens and enhancing their moral integrity, which is considered a task within the government's remit ("Xi Jinping's report at 19th CPC National Congress" 2017). In view of the government, AI can be used to this end.

The AIDP highlights AI's potential for understanding group cognition and psychology (2017). The intention to rely on AI for moral governance can be seen in further legislation, with perhaps the clearest example being the State Council's 'Outline for the Establishment of a Social Credit System', released in 2014. This document underscored that the Social Credit System did not just aim to regulate financial and corporate actions of business and citizens, but also the social behaviour of individuals. This document outlines several social challenges that the plan seeks to alleviate, including tax evasion, food safety scares, and academic dishonesty (Chorzempa et al. 2018). As highlighted, current efforts to implement these systems have been fractious, yet a number have already included moral elements, such as publicly shaming bad debtors (Hornby 2019).

Further concrete examples of how China has been utilising AI in social governance can be seen in the sphere of internal security and policing. China has been at the forefront of the development of smart cities, with approximately half of the world's smart cities located within China. The majority of resources that have gone into developing these cities have focused on surveillance technologies, such as facial recognition and cloud computing for ordinary policing (Anderlini 2019). The use of advanced 'counterterrorism'9 surveillance programmes in the autonomous region of Xinjiang offers clearer and more problematic evidence of governmental efforts to use AI for internal surveillance. This technology is not limited to facial recognition, but also includes mobile phone applications to track the local Uyghur population, who are portrayed by the government as potential dissidents or terrorists ("China's Algorithms of Repression" 2019). When government statements are read in parallel with these developments, it seems likely that some form of the Social Credit System(s) will play a central role in the future of China's AI-enabled governance (Ding 2018), putting the rights of citizens under a sharp devaluative pressure. For example, most citizens generate large data footprints, and nearly all day-to-day transactions in cities are cashless and done with mobile apps (Morris 2019), internet providers enact ‛real-name registration', linking all online activity to the individual (Sonnad 2017), enabling the government to identify and have access to the digital profile of all citizens using mobile-internet services.

The significant and likely risks related to implementing AI for governance stem from the intertwining of the material aspects of social governance with surveillance and moral control. Articles in the Western media often emphasise the problematic nature of 'the' Social Credit System, due to the authoritarian undertones of this pervasive control (Clover 2016; Botsman 2017). Examples of public dissatisfaction with specific features of locally run social credit systems appear to support this viewpoint (Zhang and Han 2019). In some cases, there have even been cases of public backlash leading to revisions in the rating criteria for local social credit systems. In contrast, some commentators have emphasised that, domestically, a national social credit system may be positively received as a response to the perception of moral decline in China, and a concomitant desire to build greater trust; indeed, it has been suggested that the system may be better named the 'Social Trust' system (Song 2018; Kobie 2019). When looking at the punishments distributed by social credit systems, some measures, including blacklisting citizens from travelling due to poor behaviour on trains, have received a positive response on Chinese social media. Government censorship and a chilling effect could account for this support, but there is currently no evidence of censors specifically targeting posts concerning social credit systems (Koetse 2018).

Efforts have also been made to understand public opinion on the systems as a whole, rather than just specific controversies or cases of blacklisting. A nationwide survey by a Western academic on China's social credit systems found high levels of approval within the population (Kostka 2019). With this said, problems with the methodology of the paper, in particular with the translation of 'social credit system', indicate that it may be more appropriate to consider a general lack of awareness, rather than a widespread sentiment of support ('Beyond Black Mirror' 2019). These points indicate that it is too early to measure public sentiment in China surrounding the development of the Social Credit System(s).

4 The debate on digital ethics and AI in China

In line with this publication, the Standardization Administration of the People's Republic of China, the national-level body responsible for developing technical standards, released a white paper on AI standards. The paper contains a discussion of the safety and ethical issues related to technology (Ding and Triolo 2018). Three key principles for setting the ethical requirements of AI technologies are outlined. First, the principle of human interest states that the ultimate goal of AI is to benefit human welfare. Second, the principle of liability emphasises the need to establish accountability as a requirement for both the development and the deployment of AI systems and solutions. Subsumed within this principle is transparency, which supports the requirement of understanding what the operating principles of an AI system are. Third, the principle of consistency of [sic] rights and responsibilities emphasised that, on the one hand, data should be properly recorded and oversight present but, on the other hand, that commercial entities should be able to protect their intellectual property (Ding and Triolo 2018).

Government affiliated bodies and private companies have also developed their own AI ethics principles. For example, the Beijing Academy of Artificial Intelligence, a research and development body including China's leading companies and Beijing universities, was established in November 2018 (Knight 2019). This body then released the 'Beijing AI Principles' to be followed for the research and development, use, and governance of AI ("Beijing AI Principles" 2019). Similar to the principles forwarded by the AIDP Expert Committee, the Beijing Principles focus on doing good for humanity, using AI 'properly', and having the foresight to predict and adapt to future threats. In the private sector, one of the most high-profile ethical framework has come from the CEO of Tencent, Pony Ma. This framework emphasises the importance of AI being available, reliable, comprehensible, and controllable (Si 2019). Finally, the Chinese Association for Artificial Intelligence (CAII)10 has yet to establish ethical principles but did form an AI ethics committee in mid-2018 with this purpose in mind ("AI association to draft ethics guidelines" 2019).

In the following sections, we shall focus on the debate about AI ethics as it is emerging in connection with privacy and medical ethics because these are two of the most mature areas where one may grasp a more general sense of the current 'Chinese approach' to digital ethics. The analysis of the two areas is not meant to provide an exhaustive map of all the debates about ethical concerns over AI in China. Instead, it may serve to highlight some of the contentious issues that are emerging, and inform a wider understanding of the type of boundaries which may be drawn in China when a normative agenda in the country is set.

4.1 Privacy

In reality, the weakness in China's privacy legislation is due less to its 'non-legally binding' status and more to the many loopholes in it, the weakness of China's judicial system, and the influential power of the government, which is often the last authority, not held accountable through democratic mechanisms. In particular, significant and problematic exemptions are present for the collection and use of data, including when related to security, health, or the vague and flexibly interpretable 'significant public interests'. It is these large loopholes that are most revealing of China's data policy. It may be argued that some broad consumer protections are present, but actually this is not extended to the government (Sacks and Laskai 2019). Thus, the strength of privacy protection is likely to be determined by the government's decisions surrounding data collection and usage, rather than legal and practical constraints. This is alarming.

It is important to recognise that the GDPR contains a similar 'public interest' basis for lawfully processing personal data where consent or anonymisation are impractical and that these conditions are poorly defined in legislation and often neglected in practice (Stevens 2017). But the crucial and stark difference between the Chinese and EU examples concerns the legal systems underpinning the two approaches. The EU's judicial branch has substantive influence, including the capacity to interpret legislation and to use judicial review mechanisms to determine the permissibility of legislation more broadly.11 In contrast, according to the Chinese legal system, the judiciary is subject to supervision and interference from the legislature, which has de jure legislative supremacy (Ji 2014); this gives de facto control to the Party (Horsley 2019). Thus, the strength of privacy protections in China may be and often is determined by the government's decisions surrounding data collection and usage rather than legal and practical constraints. As it has been remarked, 'The function of law in governing society has been acknowledged since 2002, but it has not been regarded as essential for the CCP. Rather, morality and public opinion concurrently serve as two alternatives to the law for the purpose of governance. As a result, administrative agencies may ignore the law on the basis of party policy, morality, public opinion, or other political considerations' (Wang and Liu 2019, p. 6).

4.2 Medical ethics

1. (a)
People in rural areas are the top priority

2. (b)
Disease prevention must be placed first

3. (c)
Chinese traditional medicine and Western medicine must work together

4. (d)
Health affairs must depend on science and education

5. (e)
Society as a whole should be mobilised to participate in health affairs, thus contributing to the people's health and the country's overall development.

All five principles are relevant for understanding China's healthcare system as a whole but, from the perspective of analysing the ethics of China's use of AI in the medical domain, principles (a), (b), and (e) are the most important. They highlight that-in contrast to the West, where electronic healthcare data are predominantly focused on individual health, and thus AI techniques are considered crucial to unlock 'personalised medicine' (Nittas et al. 2018)-in China, healthcare is predominantly focused on the health of the population. In this context, the ultimate ambition of AI is to liberate data for public health purposes12 (Li et al. 2019a, b). This is evident from the AIDP, which outlines the ambition to use AI to 'strengthen epidemic intelligence monitoring, prevention and control,' and to 'achieve breakthroughs in big data analysis, Internet of Things, and other key technologies' for the purpose of strengthening community intelligent health management. The same aspect is even clearer in the State Council's 2016 official notice on the development and use of big data in the healthcare sector, which explicitly states that health and medical big data sets are a national resource and that their development should be seen as a national priority to improve the nation's health (Zhang et al. 2018).13

The previous 'interpretation from within' does not imply that China's approach to the use of AI in healthcare is acceptable or raises no ethical concerns. The opposite is actually true. In particular, the Chinese approach is undermined by at least three main risks.

First, there is a risk of creating a market for human care. China's two-tiered medical system provides state-insured care for all, and the option for individuals to pay privately for quicker or higher quality treatment. This is in keeping with Confucian thought, which encourages the use of private resources to benefit oneself and one's family (Wu and Mao 2017). With the introduction of Ping An [sic] Good Doctor's unmanned '1-min clinics' across China (of which there are now as many as 1,000 in place), patients can walk in, provide symptoms and medical history, and receive an automated diagnosis and treatment plans (which are only followed up by human clinical advice for new customers), it is entirely possible to foresee a scenario in which only those who are able to pay will be able to access human clinicians. In a field where emotional care, and involvement in decision making, are often as important as the logical deduction of a 'diagnosis,' this could have a significantly negative impact on the level and quality of care accessed across the population and on the integrity of the self (Andorno 2004; Pasquale 2015),14 at least for those who are unable to afford human care.

The third ethical risk is associated with CRISPR gene modification and AI. CRISPR is a controversial gene modification technique that can be used to alter the presentation of genes in living organisms, for example for the purpose of curing or preventing genetic diseases. It is closely related to AI, as Machine Learning techniques can be used to identify which gene or genes need to be altered with the CRISPR method. The controversies, and potential significant ethical issues, associated with research in this area are related to the fact that it is not always possible to tell where the line is between unmet clinical need and human enhancement or genetic control (Cohen 2019). This became clear when, in November 2018, biophysics researcher He Jiankui revealed that he had successfully genetically modified babies using the CRISPR method to limit their chances of ever contracting HIV (Cohen 2019). The announcement was met by international outcry and He's experiment was condemned by the Chinese government at the time (Belluz 2019). However, the drive to be seen as a world leader in medical care (Cheng 2018), combined with the promise gene editing offers for the treatment of diseases, suggest that a different response may be possible in the future (Cyranoski 2019; "China opens a Pandora's Box", 2018). Such a change in government policy is especially likely as global competition in this field heats up. The US has announced that it is enrolling patients in a trial to cure an inherited form of blindness (Marchione 2019); and the UK has launched the Accelerating Detection of Disease challenge to create a five-million patient cohort whose data will be used to develop new AI approaches to early diagnosis and biomarker discovery (UK Research and Innovation 2019). These announcements create strong incentives for researchers in China to push regulatory boundaries to achieve quick successes (Tatlow 2015; Lei et al. 2019). China has filed the largest number of patents for gene-editing on animals in the world (Martin-Laffon et al. 2019). Close monitoring will be essential if further ethical misdemeanours are to be avoided.

5 Conclusion

In this article, we analysed the nature of AI policy within China and the context within which it has emerged, by mapping the major national-level policy initiatives that express the intention to utilise AI. We identified three areas of particular relevance: international competitiveness, economic growth, and social governance (construction). The development and deployment of AI in each of these areas have implications for China and for the international community. For example, although the 'trump-card' policy to gain a military advantage may not be something new, its application to AI technologies risks igniting an arms race and undermining international stability (Taddeo and Floridi 2018). Efforts to counteract this trend seem largely hollow. Our analysis indicates that China has some of the greatest opportunities for economic benefit in areas like automation and that the country is pushing forward in AI-related areas substantially. Nonetheless, efforts to cushion the disruptions that emerge from using AI in the industry are currently lacking. Thus, AI can help foster increased productivity and high levels of growth, but its use is likely to intensify the inequalities present within society and even decrease support for the government and its policies. The AIDP also promotes AI as a way to help deal with some of the major social problems, ranging from pollution to standards of living. However, positive impact in this area seem to come with increased control over individuals' behaviour, with governance extending into the realm of moral behaviour and further erosion of privacy.

Ethics also plays a central role in the Chinese policy effort on AI. The AIDP outlines a clear intention to define ethical norms and standards, yet efforts to do so are at a fledgling stage, being broadly limited to high-level principles, lacking implementation. Analyses of existing Chinese approaches and emerging debates in the areas of privacy and medical ethics provide an insight into the types of frameworks that may emerge. With respect to privacy, on the surface, recently introduced protections may seem robust, with definitions of sensitive personal information even broader than that used within the GDPR. However, a closer look exposes the many loopholes and exceptions that enable the government (and companies implicitly endorsed by the government) to bypass privacy protection and fundamental issues concerning lack of accountability and government's unrestrained decisional power about mass-surveillance.

In the same vein, when focusing on medical ethics, it is clear that, although China may agree with the West on the bioethical principles, its focus on the health of the population, in contrast to the West's focus on the health of the individual, may easily lead to unethical outcomes (the sacrifice imposed on one for the benefit of many) and is creating a number of risks, as AI encroaches on the medical space. These are likely to evolve over time, but the risks of unequal care between those who can afford a human clinician and those who cannot, control of social diseases, and of unethical medical research are currently the most significant.

China is a central actor in the international debate on the development and governance of AI. It is important to understand China's internal needs, ambitions in the international arena, and ethical concerns, all of which are shaping the development of China's AI policies. It is also important to understand all this not just externally, from a Western perspective, but also internally, from a Chinese perspective. However, some ethical safeguards, constraints and desiderata are universal and are universally accepted and cherished, such as the nature and scope of human rights.15 They enable one to evaluate, after having understood, China's approach to the development of AI. This is why in this article we have sought to contribute to a more comprehensive and nuanced analysis of the structural, cultural and political factors that ground China's stance on AI, as well as an indication of its possible trajectory, while also highlighting where ethical problems remain, arise, or are likely to be exacerbated. They should be addressed as early as it is contextually possible.

Notes

1. In the rest of this article, we shall use 'China' or 'Chinese' to refer to the political, regulatory, and governance approach decided by the Chinese national government concerning the development and use of AI capabilities.

2. The 5-year plans are a central pillar in China's economic growth policy (Heilmann and Melton 2013; Hu 2013).

3. It should be noted that, although MIST has been tasked with coordinating the AIDP, it was the Ministry of Industry and Information Technology (MIIT) that released the guidance for the implementing the first step of the AIDP.

4. To accompany the three steps outlined earlier, the Ministry of Industry and Information Technology (MIIT) provides documents to flesh out these aims. The first of these, 'Three-Year Action Plan for Promoting Development of a New Generation Artificial Intelligence Industry (2018-2020)', has already been released.

5. 'This term refers to 'an actor, which lags behind its competitors in terms of development, coming up with a radical innovation that will allow it to overtake its rivals' (Brezis et al. 1993).'

6. The Chinese text (社会建设) directly translates to 'society/community' and 'build/construction'.

7. It is worth highlighting, however, that the Chinese are more than double the world average, and ranked first, when it comes to answering the question "whether the country is going in the right direction", with 94% of the respondents in agreement.

8. The word 'counterterrorism' started to be used after 9/11, with the phrase 'cultural integration' favoured before this ("Devastating Blows 2005).

9. The Chinese Association for Artificial Intelligence (CAAI) is the only state-level science and technology organization in the field of artificial intelligence under the Ministry of Civil Affairs.

10. As a practical example of this, the Court of Justice of the European Union gave judgment in Rīgas Case (2017) that has been used in defining what is meant by 'legitimate interest.'.

11. This is not to imply that the West is not interested in using AI for population health management purposes, or that China is not interested in using AI for personalised health purposes. China is, for example, also developing an integrated data platform for research into precision medicine (Zhang et al. 2018). We simply mean to highlight that the order of priority between these two goals seems to differ.

12. The challenges section outlines some concrete benefits of implementing AI, illustrating some perceived gains to China. A separate (though more technological than ethical) point substantiated by the article is there is a lot of medical data which could potentially be beneficial, but the data are spread out among hospitals, not used for research, and largely unstructured.

13. Note that the emphasis on individual wellbeing must also be contextualised culturally.

14. For arguments on the universality of human rights coming from within cultural perspectives, see Chan ( 1999) on Confucianism and human rights.

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1. Oxford Internet Institute, University of Oxford, 1 St Giles', Oxford, OX1 3JS, UK

Huw Roberts, Josh Cowls, Jessica Morley, Mariarosaria Taddeo & Luciano Floridi

2. Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK

Josh Cowls, Mariarosaria Taddeo & Luciano Floridi

3. Department of Computer Science, University of Oxford, 15 Parks Rd, Oxford, OX1 3QD, UK

Vincent Wang & Luciano Floridi

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Correspondence to Luciano Floridi.

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Roberts, H., Cowls, J., Morley, J. et al. The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. AI & Soc 36, 59-77 (2021). https://doi.org/10.1007/s00146-020-00992-2
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Keywords

Artificial intelligence

China

Cyber warfare

Digital ethics

Economic growth

Governance

Innovation

International competition

New Generation Artificial Intelligence Development Plan

Policy

Privacy

Social governance

# data[].attributes.attachment_link.title
The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation - AI & SOCIETY

# data[].attributes.attachment_link.description
In July 2017, China's State Council released the country's strategy for developing artificial intelligence (AI), entitled 'New Generation Artificial Intelligence Development Plan' (新一代人工智能发展规划). This strategy outlined China's aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan (ca.

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Review

¹ Medical Education Scholarship and Research Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

² Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates

*all authors contributed equally

Corresponding Author:

Nabil Zary, PhD

Mohammed Bin Rashid University of Medicine and Health Sciences

Building 14, Dubai Healthcare City

PO Box 505055

Dubai,

United Arab Emirates

Phone: 971 83571846

Email: nabil.zary@icloud.com

Background: Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education.

Objective: This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education.

Methods: Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively.

Results: A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications.

Conclusions: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.

JMIR Med Educ 2019;5(1):e13930

doi:10.2196/13930

Keywords

The application of AI in medicine remains a hot topic of keen interest for researchers and is under constant development and refinement. One such advancement has machines capable of making a radiological diagnosis at an equal or even higher success rate than highly qualified consultants in that particular specialty. Another well-known example is IBM Watson, which has successfully morphed from its triumph in the game of "Jeopardy!" to the field of medical oncology. Apart from the highly publicized role of AI in radiological diagnosis, other applications include use as an adjunct to the ideal management of cancer or chronic illnesses such as chronic mental disorders [], particularly regarding the choice of medication with the best response and side effect profiles.

The aim of this study was therefore to review the current reported scholarly work on AI in medical education. Two research questions guided this study:

How is AI currently used in medical education?

What are the challenges in implementing AI in medical education?

We conducted an integrative review of peer-reviewed literature on AI used in medical education. Integrative reviews are the broadest type of research review method and allow for inclusion of various research designs to more fully understand a phenomenon of interest.

Data Sources and Search Strategy

We searched Medline (Ovid; 1954 to March 2019), EBSCOhost Education Resources Information Center and Education Source (1983 to March 2019), and Web of Science (1986 to March 2019) to identify articles addressing AI in medical education. We developed the search strategy in collaboration with an academic health sciences librarian. The key search terms were ("artificial intelligence" OR ai OR "machine learning" OR "deep learning") AND ("medical education" OR "medical student*" OR "medical curriculum" OR "medical school*" OR "medical training").

Inclusion and Exclusion Criteria

AI was defined as any technique that enables machines to imitate intelligent human behavior. This includes symbolic (logic based or knowledge based), statistical (probabilistic methods and machine learning), and subsymbolic (embodied intelligence, search, and optimization) AI paradigms covering different problem domains (perception, reasoning, knowledge, planning, and communication). In this study, the chosen medical education focus was on doctors' professional development across the whole education continuum, from undergraduate, postgraduate, and specialty training to continuing medical education.

Any feature of AI, such as machine learning and deep learning, was included in the search. The exclusion criteria were as follows:

Articles on other aspects of education apart from medical education

Articles on use of technology (such as online lectures and computer-based education) without incorporation of AI, or articles with only a brief mention of AI usage

Article types: reviews, letters, and commentaries

Full texts of articles available in languages other than English

Articles published before 1954 due to lack of availability of online archiving of journals

Selection of Articles for Review

The titles and abstracts of identified articles were screened for the previously identified search criteria, and exclusion criteria were applied. All articles screened to be relevant or inconclusive were assessed in full text. Data such as current application in clinical practice, advantages of such use, and challenges of implementation of AI were extracted from all relevant articles.

Data Extraction

Data were carefully evaluated and extracted from all the eligible publications. Data retrieved from the studies included the name of the AI application, the study group, the use of AI, and the challenges of implementation as shown in .

Analysis of the Articles

Studies relevant to the use of AI in medical education were assessed using the Extension of Technology Acceptance Model and the Diffusion of Innovations theory [] to explain the adoption of AI in medical education; data were subsequently pooled together and analyzed quantitatively using a statistical software (IBM SPSS Statistics for Windows, version 24.0; Chicago, IL), where relevant.

Table 1. Integrative review of the included studies.

Author and year

AI a application

Study group

Use of AI

Challenges of implementation

Clancey and Stanford Univ, 1983 []

GUIDON

Guides students to solve problems on infectious diseases using a diagnostic problem-solving approach

The need for a structured set of production rules

Papa et al, 1992 []

UG

Assess medical students' diagnostic capabilities

Need to create algorithms for different symptom approach

Billinghurst et al, 1996 []

Prototype sinus surgery

Provides an intelligent simulation tool or surgical assistant

Requires improved script activation for immediate recognition of surgeon's actions with an appropriate response

Bourlas et al, 1996 []

CARDIO-LOGOS

Assists learners in the recognition and diagnosis of ECG g patterns

Variability in diagnostic criteria of ECG amongst different groups of specialists

Caudell et al, 2003 []

Project TOUCH

N/A

Real-time AI simulation engine in a 3D environment with VR h in a virtual patient

Need to validate the effectiveness of the AI system,
network congestions may disrupt group interactions

Cao et al, 2015 []

PG

Provides an effective training platform for anesthetists using a VR environment

The need for a multidisciplinary team: Anesthetists are unable to process data in an engineering way, and engineers are unable to produce clinically interpretable data

Chen et al, 2018 []

Trove radiology resident dashboard

PG

Automates ICD j/CPT k classification to provide a more up-to-date dashboard for radiology residents

The rule-based approach had a lack of scalability

Overview

Our search in the different databases revealed a total of 679 articles (). After removal of the duplicates (n=185), the remaining 494 unique articles were screened based on the title and abstract; of those, 416 articles were excluded, as they were not about AI or medical education. Of the remaining 78 articles that were assessed for eligibility, 37 articles were found to be relevant to the use of AI in medical education. Three primary uses of AI in medical education were identified: use of AI as learning support (n=32), assessment of learning (n=4), curriculum review (n=1). Of the 37 articles, 34 articles were found to be relevant to the challenges of implementation of AI in medical education.

Current Use of Artificial Intelligence in Medical Education

Data gathered from the articles such as advantages of the current uses as well as the challenges of implementation of AI in medical education mentioned in the articles were pooled together and analyzed quantitatively.

One article discussed the use of AI, artificial neural networks (ANN), and support vector machine (SVM) specifically, for assessing the curriculum of medical students. Chen et al [] described the advantage of ANN and SVM over logistic regression in data analysis: They are more adept models for solving nonlinear problems and establishing relationships between variables. The use of AI in assessing the curriculum of medical education can provide an overview of the effectiveness and students' satisfaction with the program, which is paramount in training future doctors in medical diagnosis and treatment.

In addition, there were three main target groups identified in the 37 reviewed articles (): medical undergraduates (n=25), postgraduates (n=14), and those continuing medical education (CME; n=8). No specific target group was identified in 6 of the articles; all participants were referred to as "students."

Challenges in Implementing Artificial Intelligence in Medical Education

The critical elements of the Diffusion of Innovations model used to assess the challenges include innovation, communications channel, time, and the social system. The challenges described in the articles mainly centered around the innovation itself (). This includes the technological difficulties in acquiring a large sample size needed for the development of the model (n=16), the requirement for a qualified and experienced content specialist to design the curriculum for machine learning (n=10), and the communication challenges relative to the knowledge gap between physicians and engineers (n=4).

In this study, we reported the roles and advantages of AI in medical education as well as the challenges that have been hindering the widespread implementation of AI in the medical education community. This section will discuss the main findings.

Current Use of Artificial Intelligence in Medical Education

A review of the curriculum is an administrative and arduous process, which strongly speaks to the need for machine automation to ease the process. Interestingly, only one of the reviewed articles [] described the use of AI for medical curriculum review. There is a lack of use of AI in curriculum review despite the advantages AI may have over traditional methods, such as the use of a logistic regression model. Examples of these advantages include the ability of ANNs to solve multidimensional problems, provide greater classification accuracy, and establish strong relationships between variables. One plausible reason for the lack of adoption of AI in curriculum review is the limited digitalization in medical education learning management systems, which is essential for creation of a curriculum map. The digitalization of the curriculum is not possible across all institutions, mainly due to financial constraints []. Notably, in Canada and the United Kingdom [], the majority of medical schools are building curriculum maps. Evidently, as shown in the Results section above (), a large pool of data is required to adequately support the development of the model for an AI system. Currently, there are two main approaches to obtaining data-accessing records from prior digitalization of the curriculum and transferring hard copy data into a soft copy, which is a time-consuming process. The latter may well explain the lack of interest in the application of AI with respect to the curriculum in medical education.

In the subgroup analysis of the articles on learning, medical undergraduates were the primary target audience (21/32) with a lesser focus on CME. This is an exciting finding because undergraduate education forms only a small proportion of a doctor's professional development. A plausible reason for this finding is the lack of a structured curriculum for CME, with a strong emphasis on professional self-regulation []. The development of an AI system requires expert domain knowledge to equip the system with the appropriate curriculum knowledge for contextually driven education.

Without a structured curriculum, it is difficult to select a knowledge base for the AI system, which may, in part, explain the lower prevalence of AI use in CME as compared to that in medical undergraduates. Another possible reason for the preferred focus of AI in undergraduate medical education is that it enables shaping of students' learning at an earlier point of their medical career. A study by Shin et al [] demonstrated that undergraduates who adopted problem-based learning are more up to date in medical information as compared to their counterparts who experienced a traditional curriculum. The use of AI enhances problem-based learning, because it provides step-by-step guidance with the appropriate feedback, possibly explaining the preference for targeting medical undergraduates.

Similar to the use of AI in curriculum delivery, only a minority of the articles (4/37) discussed its use in assignments, which begs the question: Why? There are many advantages to using AI in assessments, not the least of which is the ability to provide immediate and formative feedback to students (2/4), which is also the most common reason for the use of AI in learning (21/32). However, the reason for its lack of use is also likely related to the lack of digitalization, explained above in the reasons for the lack of current use of AI for curriculum review. There are many forms of examination in medicine, the most common of which is the written examination and the Objective Structured Clinical Examinations []. These methods of assessment are usually conducted offline using pen and paper.

Table 2. Overview of the current uses of artificial intelligence in medical education identified from review of 37 full-text articles.

Challenges of Implementation of Artificial Intelligence in Medical Education

This section will discuss the two main groups of challenges hindering the implementation of AI: (1) limitations in the perceived usefulness of AI and (2) the technical difficulties with the development of AI applications.

Difficulty in assessing the effectiveness of the AI application was the most commonly reported challenge (14/34). To prove the effectiveness of the AI system, an ideal approach would involve scientific rigor and the ability to explore what AI does ("explainability"). The issue of "explainability" is specific for deep learning, which is a subset of AI. Due to the nonlinear nature of deep learning, there is often no explanation of how the AI system arrives at the answer or prediction []. However, an explanation of the thought process is crucial for students' learning, especially in medical education, where clinical reasoning forms the foundation of a doctor's professional development.

An essential aspect of developing an AI system is the need for a multidisciplinary team (included in 4 of 34 studies; ), including educational experts, data scientists for management of the large pool of data, physicians for ensuring the clinical relevance, and accuracy of the AI system. Engineers and data scientists are more focused on the accuracy of the AI system to determine how likely the system is to predict a result correctly. However, this still may have little clinical and educational relevance. If this relevance is to be attained, the medical domain and educational experts will increasingly need to work in concert with data scientists in order to develop AI systems that are both accurate and effective in medical education.

The Learners' Data Integrity

Worth noting is that only two of the reviewed articles [,] raised the issue of privacy and confidentiality. In the world of digitalization, data protection is paramount. This is seen in the rise of statutory laws such as the Data Protection Act 2018 (United Kingdom) [] and the Personal Data Protection Act 2012 (Singapore) []. Data are protected, especially if AI practice is conducted in commercial settings where companies profit from gathering data. Concurrently, there is a need to develop novel models that allow access to educational data for the development of AI applications [].

For example, data of learners undergoing CME may be used as a factor for determining performance. Any leakage or manipulation of these data may adversely affect the promotion of doctors. The lack of a robust data protection measure places learners' data (which is often used to train AI algorithms) at risk and may well lead to societal rejection of the use of AI in medical education, a key element in the Diffusion of Innovations theory. It is therefore necessary to consider data security in addition to the perceived usefulness and technical difficulties of an AI system.

The Ability of Artificial Intelligence Systems to Address Ethical Issues

Introduction of Artificial Intelligence Into the Medical Profession

One of the difficulties experienced in the implementation of AI in medical education is the gap in knowledge between physicians and engineers (), which leads to this question: Should AI be introduced to medical professionals and trainees, and if so, how? This is the ideal situation that would solve one of the significant difficulties of implementing AI. This has been recently discussed by Kolachalama and Garg []; the current medical school curriculum is unable to accommodate AI due to two main reasons-insufficient time and lack of expertise. Although we acknowledge the difficulty of teaching AI in the short 5-6 years of medical school, tweaks can be made to the curricula to introduce the concepts of AI alongside traditional medical school teaching. For instance, AI applications such as CARDIO-LOGOS () can be introduced to teach clinical students the diagnostic approach of reading electrocardiograms, but students can also be simultaneously taught the algorithm the machine uses to maximize their learning when introduced to the application. Other techniques to introduce AI include abstinence from jargon and highlighting the application of AI in the diagnosis and management of real patients []. Another factor that has been raised earlier is the lack of expertise to teach AI in the medical profession. An easy way to do so is to collaborate with engineering and computing faculties and seek their professional opinions. Interfaculty collaborations and competitions can also be held in universities to promote interaction between students and peers and allow the sharing of expertise across different fields such as health care hackathons, which have been increasing in recent years [].

Future Research

Based on the findings from our review, we propose that future research should focus on assessing the effectiveness of AI in medical education. Only one study that reviewed expert-led training in rheumatology has thus far shown the benefit of the use of an AI-driven system as compared to traditional methods. Given that the diagnostic approach varies across specialties, intensive and time-consuming research is still needed in every subspecialty to truly determine the success of AI systems as compared to traditional approaches.

With the increased use of AI systems made possible through the evolving digitalization of the medical curriculum and collaboration between data scientists and physicians, the issue of data protection will need to be researched with an emphasis on how best to improve data security and increase users' confidence of the use of AI applications.

As technology continues to advance, the potential uses of AI will continue to increase in medical education. One such development will be the use of AI, combined with immersive technologies such as virtual reality and augmented reality. As presented in our results, such studies have already been reported. Further research should explore more complex adaptations of AI in medical education.

Limitations

The scope of this review covers a broad spectrum of the current applications of AI in medical education. In the field of medicine, where the practices of each subspecialty vary tremendously, the use of AI in education may also vary. It may therefore be too early to make an overarching statement about the benefits of AI in medical education. One limitation in the interpretation of the results is the high proportion of articles on the use of AI as a support for learning, as compared to its use in support of the development and review of the curriculum. Another is in the summative assessment of learners' performance. Although these may be representative of the current uses of AI, the conclusions drawn from the uses of AI in the curriculum and assessment may be inconsequential due to the low number of studies reviewed.

Conclusions

This review identified the current uses of AI in medical education, which include curriculum assessment and improvement of students' learning, with research mainly existing on the latter. The studies also highlighted the main challenges hindering the implementation of AI in medical education, which relate to how best to assess the effectiveness of AI and to manage the technical difficulties associated with the effective and productive development of an AI system.

Acknowledgments

We extend a special note of appreciation to the Medical Education Research and Scholarship Unit at the Lee Kong Chian School of Medicine for providing logistical support. We wish to thank Paul Gagnon for valuable feedback on drafts of the manuscript and Tor Sandén for improving the figures.

Conflicts of Interest

None declared.
‎
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Edited by G Eysenbach; submitted 06.03.19; peer-reviewed by WH Ho, V Kolachalama; comments to author 31.03.19; revised version received 15.04.19; accepted 16.04.19; published 15.06.19

©Kai Siang Chan, Nabil Zary. Originally published in JMIR Medical Education (http://mededu.jmir.org), 15.06.2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on http://mededu.jmir.org/, as well as this copyright and license information must be included.

Background: Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education.

Objective: This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education.

Methods: Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively.

Results: A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications.

Conclusions: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.

JMIR Med Educ 2019;5(1):e13930

doi:10.2196/13930

Keywords

The application of AI in medicine remains a hot topic of keen interest for researchers and is under constant development and refinement. One such advancement has machines capable of making a radiological diagnosis at an equal or even higher success rate than highly qualified consultants in that particular specialty. Another well-known example is IBM Watson, which has successfully morphed from its triumph in the game of "Jeopardy!" to the field of medical oncology. Apart from the highly publicized role of AI in radiological diagnosis, other applications include use as an adjunct to the ideal management of cancer or chronic illnesses such as chronic mental disorders [], particularly regarding the choice of medication with the best response and side effect profiles.

In this age of rapidly advancing technology, the need to ground novel work on reported research is vital in order to advance the field of AI in medical education. Currently, there are limited articles [,] discussing or reviewing the current applications of AI in medical education.

The aim of this study was therefore to review the current reported scholarly work on AI in medical education. Two research questions guided this study:

How is AI currently used in medical education?

What are the challenges in implementing AI in medical education?

We conducted an integrative review of peer-reviewed literature on AI used in medical education. Integrative reviews are the broadest type of research review method and allow for inclusion of various research designs to more fully understand a phenomenon of interest.

Data Sources and Search Strategy

We searched Medline (Ovid; 1954 to March 2019), EBSCOhost Education Resources Information Center and Education Source (1983 to March 2019), and Web of Science (1986 to March 2019) to identify articles addressing AI in medical education. We developed the search strategy in collaboration with an academic health sciences librarian. The key search terms were ("artificial intelligence" OR ai OR "machine learning" OR "deep learning") AND ("medical education" OR "medical student*" OR "medical curriculum" OR "medical school*" OR "medical training").

Inclusion and Exclusion Criteria

AI was defined as any technique that enables machines to imitate intelligent human behavior. This includes symbolic (logic based or knowledge based), statistical (probabilistic methods and machine learning), and subsymbolic (embodied intelligence, search, and optimization) AI paradigms covering different problem domains (perception, reasoning, knowledge, planning, and communication). In this study, the chosen medical education focus was on doctors' professional development across the whole education continuum, from undergraduate, postgraduate, and specialty training to continuing medical education.

Any feature of AI, such as machine learning and deep learning, was included in the search. The exclusion criteria were as follows:

Articles on other aspects of education apart from medical education

Articles on use of technology (such as online lectures and computer-based education) without incorporation of AI, or articles with only a brief mention of AI usage

Article types: reviews, letters, and commentaries

Full texts of articles available in languages other than English

Articles published before 1954 due to lack of availability of online archiving of journals

Selection of Articles for Review

The titles and abstracts of identified articles were screened for the previously identified search criteria, and exclusion criteria were applied. All articles screened to be relevant or inconclusive were assessed in full text. Data such as current application in clinical practice, advantages of such use, and challenges of implementation of AI were extracted from all relevant articles.

Data Extraction

Data were carefully evaluated and extracted from all the eligible publications. Data retrieved from the studies included the name of the AI application, the study group, the use of AI, and the challenges of implementation as shown in .

Analysis of the Articles

Studies relevant to the use of AI in medical education were assessed using the Extension of Technology Acceptance Model and the Diffusion of Innovations theory [] to explain the adoption of AI in medical education; data were subsequently pooled together and analyzed quantitatively using a statistical software (IBM SPSS Statistics for Windows, version 24.0; Chicago, IL), where relevant.

Table 1. Integrative review of the included studies.

Author and year

AI a application

Study group

Use of AI

Challenges of implementation

Clancey and Stanford Univ, 1983 []

GUIDON

Guides students to solve problems on infectious diseases using a diagnostic problem-solving approach

The need for a structured set of production rules

Papa et al, 1992 []

UG

Assess medical students' diagnostic capabilities

Need to create algorithms for different symptom approach

Billinghurst et al, 1996 []

Prototype sinus surgery

Provides an intelligent simulation tool or surgical assistant

Requires improved script activation for immediate recognition of surgeon's actions with an appropriate response

Bourlas et al, 1996 []

CARDIO-LOGOS

Assists learners in the recognition and diagnosis of ECG g patterns

Variability in diagnostic criteria of ECG amongst different groups of specialists

Caudell et al, 2003 []

Project TOUCH

N/A

Real-time AI simulation engine in a 3D environment with VR h in a virtual patient

Need to validate the effectiveness of the AI system,
network congestions may disrupt group interactions

Cao et al, 2015 []

PG

Provides an effective training platform for anesthetists using a VR environment

The need for a multidisciplinary team: Anesthetists are unable to process data in an engineering way, and engineers are unable to produce clinically interpretable data

Chen et al, 2018 []

Trove radiology resident dashboard

PG

Automates ICD j/CPT k classification to provide a more up-to-date dashboard for radiology residents

The rule-based approach had a lack of scalability

Overview

Our search in the different databases revealed a total of 679 articles (). After removal of the duplicates (n=185), the remaining 494 unique articles were screened based on the title and abstract; of those, 416 articles were excluded, as they were not about AI or medical education. Of the remaining 78 articles that were assessed for eligibility, 37 articles were found to be relevant to the use of AI in medical education. Three primary uses of AI in medical education were identified: use of AI as learning support (n=32), assessment of learning (n=4), curriculum review (n=1). Of the 37 articles, 34 articles were found to be relevant to the challenges of implementation of AI in medical education.

Current Use of Artificial Intelligence in Medical Education

Data gathered from the articles such as advantages of the current uses as well as the challenges of implementation of AI in medical education mentioned in the articles were pooled together and analyzed quantitatively.

One article discussed the use of AI, artificial neural networks (ANN), and support vector machine (SVM) specifically, for assessing the curriculum of medical students. Chen et al [] described the advantage of ANN and SVM over logistic regression in data analysis: They are more adept models for solving nonlinear problems and establishing relationships between variables. The use of AI in assessing the curriculum of medical education can provide an overview of the effectiveness and students' satisfaction with the program, which is paramount in training future doctors in medical diagnosis and treatment.

A total of 32 articles discussed the use of AI platforms or systems explicitly designed to improve students' learning (), and 7 articles discussed the use of AI as an adjunct to a virtual environment or simulation for trainees, a majority of which are most relevant to the surgical specialty. The TOUCH project, LAHYSTOTRAIN, and EchoComJ are examples of systems developed using an intelligent tutoring system alongside a virtual reality simulation program. These systems provide added benefits of a virtual environment alongside the benefits of an intelligent tutoring system, including immersive, interactive, and safe environments in a VR simulation, as described by Caudell et al [].

Four articles examined the use of machine learning models in the assessment of students' learning (). Three articles assessed the use of AI in automated scoring of assignments, and one article [] assessed the use of machine learning algorithms in predicting the correctness of students' answers based on their viewing behaviors. Common advantages include an objective assessment of students' work, more cost-effectiveness and time efficiency, and the ability to provide immediate feedback on their assignment, allowing students to reflect on their work.

In addition, there were three main target groups identified in the 37 reviewed articles (): medical undergraduates (n=25), postgraduates (n=14), and those continuing medical education (CME; n=8). No specific target group was identified in 6 of the articles; all participants were referred to as "students."

Challenges in Implementing Artificial Intelligence in Medical Education

The critical elements of the Diffusion of Innovations model used to assess the challenges include innovation, communications channel, time, and the social system. The challenges described in the articles mainly centered around the innovation itself (). This includes the technological difficulties in acquiring a large sample size needed for the development of the model (n=16), the requirement for a qualified and experienced content specialist to design the curriculum for machine learning (n=10), and the communication challenges relative to the knowledge gap between physicians and engineers (n=4).

In this study, we reported the roles and advantages of AI in medical education as well as the challenges that have been hindering the widespread implementation of AI in the medical education community. This section will discuss the main findings.

Current Use of Artificial Intelligence in Medical Education

A review of the curriculum is an administrative and arduous process, which strongly speaks to the need for machine automation to ease the process. Interestingly, only one of the reviewed articles [] described the use of AI for medical curriculum review. There is a lack of use of AI in curriculum review despite the advantages AI may have over traditional methods, such as the use of a logistic regression model. Examples of these advantages include the ability of ANNs to solve multidimensional problems, provide greater classification accuracy, and establish strong relationships between variables. One plausible reason for the lack of adoption of AI in curriculum review is the limited digitalization in medical education learning management systems, which is essential for creation of a curriculum map. The digitalization of the curriculum is not possible across all institutions, mainly due to financial constraints []. Notably, in Canada and the United Kingdom [], the majority of medical schools are building curriculum maps. Evidently, as shown in the Results section above (), a large pool of data is required to adequately support the development of the model for an AI system. Currently, there are two main approaches to obtaining data-accessing records from prior digitalization of the curriculum and transferring hard copy data into a soft copy, which is a time-consuming process. The latter may well explain the lack of interest in the application of AI with respect to the curriculum in medical education.

Interestingly, the question remains: Would that compromise the quality of the feedback received? Useful feedback should essentially assist students in identifying conceptual misunderstandings, critique their performance, and be structured enough to help students achieve their learning objectives []. One of the limitations of automated and immediate feedback provision with AI is limitation in the quality of the feedback [], as the feedback generated is based on the knowledge base and model of the AI system, which, as of now, has room for improvement.

In the subgroup analysis of the articles on learning, medical undergraduates were the primary target audience (21/32) with a lesser focus on CME. This is an exciting finding because undergraduate education forms only a small proportion of a doctor's professional development. A plausible reason for this finding is the lack of a structured curriculum for CME, with a strong emphasis on professional self-regulation []. The development of an AI system requires expert domain knowledge to equip the system with the appropriate curriculum knowledge for contextually driven education.

Without a structured curriculum, it is difficult to select a knowledge base for the AI system, which may, in part, explain the lower prevalence of AI use in CME as compared to that in medical undergraduates. Another possible reason for the preferred focus of AI in undergraduate medical education is that it enables shaping of students' learning at an earlier point of their medical career. A study by Shin et al [] demonstrated that undergraduates who adopted problem-based learning are more up to date in medical information as compared to their counterparts who experienced a traditional curriculum. The use of AI enhances problem-based learning, because it provides step-by-step guidance with the appropriate feedback, possibly explaining the preference for targeting medical undergraduates.

Similar to the use of AI in curriculum delivery, only a minority of the articles (4/37) discussed its use in assignments, which begs the question: Why? There are many advantages to using AI in assessments, not the least of which is the ability to provide immediate and formative feedback to students (2/4), which is also the most common reason for the use of AI in learning (21/32). However, the reason for its lack of use is also likely related to the lack of digitalization, explained above in the reasons for the lack of current use of AI for curriculum review. There are many forms of examination in medicine, the most common of which is the written examination and the Objective Structured Clinical Examinations []. These methods of assessment are usually conducted offline using pen and paper.

Table 2. Overview of the current uses of artificial intelligence in medical education identified from review of 37 full-text articles.

Challenges of Implementation of Artificial Intelligence in Medical Education

This section will discuss the two main groups of challenges hindering the implementation of AI: (1) limitations in the perceived usefulness of AI and (2) the technical difficulties with the development of AI applications.

Difficulty in assessing the effectiveness of the AI application was the most commonly reported challenge (14/34). To prove the effectiveness of the AI system, an ideal approach would involve scientific rigor and the ability to explore what AI does ("explainability"). The issue of "explainability" is specific for deep learning, which is a subset of AI. Due to the nonlinear nature of deep learning, there is often no explanation of how the AI system arrives at the answer or prediction []. However, an explanation of the thought process is crucial for students' learning, especially in medical education, where clinical reasoning forms the foundation of a doctor's professional development.

What is clinical reasoning? It was initially described by Barrows [] as "the cognitive process that is necessary to evaluate and manage a patient's medical problem." Clinical problems are often ill-structured and multifaceted, which explains the need for a comprehensive history from a patient. These factors are then taken into consideration, and a clinical impression is formed, which then enables the construction of differential diagnoses. As this is a much more complex process than providing a simple diagnosis label based on the symptom complex of a patient, the use of deep learning reaches the cognitive limits to aid in medical education. In addition, to objectively prove the effectiveness of AI, studies need to compare the use of AI with traditional methods of teaching. These studies require a large sample size for the results to be probabilistic. Clear surrogate markers such as pretest and posttest scores are fundamental to analyze the results objectively. Study subjects should also have a similar level of understanding of the topic taught before any intervention. As a result, limited studies [] have been conducted to assess the effectiveness of AI in medical education.

An essential aspect of developing an AI system is the need for a multidisciplinary team (included in 4 of 34 studies; ), including educational experts, data scientists for management of the large pool of data, physicians for ensuring the clinical relevance, and accuracy of the AI system. Engineers and data scientists are more focused on the accuracy of the AI system to determine how likely the system is to predict a result correctly. However, this still may have little clinical and educational relevance. If this relevance is to be attained, the medical domain and educational experts will increasingly need to work in concert with data scientists in order to develop AI systems that are both accurate and effective in medical education.

The Learners' Data Integrity

Worth noting is that only two of the reviewed articles [,] raised the issue of privacy and confidentiality. In the world of digitalization, data protection is paramount. This is seen in the rise of statutory laws such as the Data Protection Act 2018 (United Kingdom) [] and the Personal Data Protection Act 2012 (Singapore) []. Data are protected, especially if AI practice is conducted in commercial settings where companies profit from gathering data. Concurrently, there is a need to develop novel models that allow access to educational data for the development of AI applications [].

For example, data of learners undergoing CME may be used as a factor for determining performance. Any leakage or manipulation of these data may adversely affect the promotion of doctors. The lack of a robust data protection measure places learners' data (which is often used to train AI algorithms) at risk and may well lead to societal rejection of the use of AI in medical education, a key element in the Diffusion of Innovations theory. It is therefore necessary to consider data security in addition to the perceived usefulness and technical difficulties of an AI system.

The Ability of Artificial Intelligence Systems to Address Ethical Issues

Introduction of Artificial Intelligence Into the Medical Profession

One of the difficulties experienced in the implementation of AI in medical education is the gap in knowledge between physicians and engineers (), which leads to this question: Should AI be introduced to medical professionals and trainees, and if so, how? This is the ideal situation that would solve one of the significant difficulties of implementing AI. This has been recently discussed by Kolachalama and Garg []; the current medical school curriculum is unable to accommodate AI due to two main reasons-insufficient time and lack of expertise. Although we acknowledge the difficulty of teaching AI in the short 5-6 years of medical school, tweaks can be made to the curricula to introduce the concepts of AI alongside traditional medical school teaching. For instance, AI applications such as CARDIO-LOGOS () can be introduced to teach clinical students the diagnostic approach of reading electrocardiograms, but students can also be simultaneously taught the algorithm the machine uses to maximize their learning when introduced to the application. Other techniques to introduce AI include abstinence from jargon and highlighting the application of AI in the diagnosis and management of real patients []. Another factor that has been raised earlier is the lack of expertise to teach AI in the medical profession. An easy way to do so is to collaborate with engineering and computing faculties and seek their professional opinions. Interfaculty collaborations and competitions can also be held in universities to promote interaction between students and peers and allow the sharing of expertise across different fields such as health care hackathons, which have been increasing in recent years [].

Future Research

Based on the findings from our review, we propose that future research should focus on assessing the effectiveness of AI in medical education. Only one study that reviewed expert-led training in rheumatology has thus far shown the benefit of the use of an AI-driven system as compared to traditional methods. Given that the diagnostic approach varies across specialties, intensive and time-consuming research is still needed in every subspecialty to truly determine the success of AI systems as compared to traditional approaches.

With the increased use of AI systems made possible through the evolving digitalization of the medical curriculum and collaboration between data scientists and physicians, the issue of data protection will need to be researched with an emphasis on how best to improve data security and increase users' confidence of the use of AI applications.

As technology continues to advance, the potential uses of AI will continue to increase in medical education. One such development will be the use of AI, combined with immersive technologies such as virtual reality and augmented reality. As presented in our results, such studies have already been reported. Further research should explore more complex adaptations of AI in medical education.

Limitations

The scope of this review covers a broad spectrum of the current applications of AI in medical education. In the field of medicine, where the practices of each subspecialty vary tremendously, the use of AI in education may also vary. It may therefore be too early to make an overarching statement about the benefits of AI in medical education. One limitation in the interpretation of the results is the high proportion of articles on the use of AI as a support for learning, as compared to its use in support of the development and review of the curriculum. Another is in the summative assessment of learners' performance. Although these may be representative of the current uses of AI, the conclusions drawn from the uses of AI in the curriculum and assessment may be inconsequential due to the low number of studies reviewed.

Conclusions

This review identified the current uses of AI in medical education, which include curriculum assessment and improvement of students' learning, with research mainly existing on the latter. The studies also highlighted the main challenges hindering the implementation of AI in medical education, which relate to how best to assess the effectiveness of AI and to manage the technical difficulties associated with the effective and productive development of an AI system.

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medical education; evaluation of aied systems; real world applications of aied systems; artificial intelligence

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Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

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Background: Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners.

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ZBW - Digitales Archiv: Artificial Intelligence in Education : The Urgent Need to Prepare Teachers for Tomorrow’s Schools

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Digital Archive is a publication server for scholarly economic literature, provided as a non-commercial public service by the ZBW.