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Langreo, Lauraine. “No, AI Won’t Destroy Education. But We Should Be Skeptical.” Education Week, vol. 43, no. 4, Sept. 2023, pp. 20–23. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&db=asn&AN=171935114&scope=site.

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Baltezarević, Radoslav, and Ivana Baltezarević. “Students’ Attitudes on The Role of Artificial Intelligence (Ai) In Personalized Learning.” International Journal of Cognitive Research in Science, Engineering & Education (IJCRSEE), vol. 12, no. 2, Aug. 2024, pp. 387–97. EBSCOhost, https://doi.org/10.23947/2334-8496-2024-12-2-387-397.

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Holcombe, Amy, and Steve “Woz” Wozniak. “Using AI to Fuel Engagement and Active Learning: Used in Combination with Creative Human Intelligence, Artificial Intelligence Can Transform the Way Students Experience School.” Educational Leadership, vol. 81, no. 9, Summer 2024, pp. 16–21. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&db=asn&AN=178452872&scope=site.

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How is artificial intelligence present in education currently?

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How is artificial intelligence present in education currently?

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Does artificial intellegince have an overall positive or negative effect on student comprehension?

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Does artificial intellegince have an overall positive or negative effect on student comprehension?

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What kind of experiments/trials have been executed to test the effectiveness of artificial intelligence in education? What kind could be in the future?

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What kind of experiments/trials have been executed to test the effectiveness of artificial intelligence in education? What kind could be in the future?

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Aoun, Joseph E. “How Higher Ed Can Adapt to the Challenges of AI.” Chronicle of Higher Education, vol. 70, no. 24, 2 Aug. 2024, pp. 1–5. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&db=asn&AN=179412457&scope=site.

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Artificial intellegence will most likely completely transform education and we as a society must adjust our curriculums correspondingly.

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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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Evolution and Revolution in Artificial Intelligence in Education - International Journal of Artificial Intelligence in Education

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The field of Artificial Intelligence in Education (AIED) has undergone significant developments over the last twenty-five years. As we reflect on our past and shape our future, we ask two main questions: What are our major strengths? And, what new opportunities lay on the horizon? We analyse 47 papers from three years in the history of the Journal of AIED (1994, 2004, and 2014) to identify the foci and typical scenarios that occupy the field of AIED. We use those results to suggest two parallel strands of research that need to take place in order to impact education in the next 25 years: One is an evolutionary process, focusing on current classroom practices, collaborating with teachers, and diversifying technologies and domains. The other is a revolutionary process where we argue for embedding our technologies within students’ everyday lives, supporting their cultures, practices, goals, and communities.

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Artificial intelligence in education: Addressing ethical challenges in K-12 settings

Abstract

Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students' learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners' behaviors. Despite the potential benefits of AI to support students' learning experiences and teachers' practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students' learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers-i.e., the Massachusetts Institute of Technology's (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students' understanding of AI and ethics.

1 Introduction

"Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks."- Stephen Hawking.

We may not think about artificial intelligence (AI) on a daily basis, but it is all around us, and we have been using it for years. When we are doing a Google search, reading our emails, getting a doctor's appointment, asking for driving directions, or getting movie and music recommendations, we are constantly using the applications of AI and its assistance in our lives. This need for assistance and our dependence on AI systems has become even more apparent during the COVID-19 pandemic. The growing impact and dominance of AI systems reveals itself in healthcare, education, communications, transportation, agriculture, and more. It is almost impossible to live in a modern society without encountering applications powered by AI [ 10, 32].

Artificial intelligence (AI) can be defined briefly as the branch of computer science that deals with the simulation of intelligent behavior in computers and their capacity to mimic, and ideally improve, human behavior [ 43]. AI dominates the fields of science, engineering, and technology, but also is present in education through machine-learning systems and algorithm productions [ 43]. For instance, AI has a variety of algorithmic applications in education, such as personalized learning systems to promote students' learning, automated assessment systems to support teachers in evaluating what students know, and facial recognition systems to provide insights about learners' behaviors [ 49]. Besides these platforms, algorithm systems are prominent in education through different social media outlets, such as social network sites, microblogging systems, and mobile applications. Social media are increasingly integrated into K-12 education [ 7] and subordinate learners' activities to intelligent algorithm systems [ 17]. Here, we use the American term "K-12 education" to refer to students' education in kindergarten (K) (ages 5-6) through 12th grade (ages 17-18) in the United States, which is similar to primary and secondary education or pre-college level schooling in other countries. These AI systems can increase the capacity of K-12 educational systems and support the social and cognitive development of students and teachers [ 55, 8]. More specifically, applications of AI can support instruction in mixed-ability classrooms; while personalized learning systems provide students with detailed and timely feedback about their writing products, automated assessment systems support teachers by freeing them from excessive workloads [ 26, 42].

Toward this end, the existing literature provides little guidance and contains a limited number of studies that focus on supporting K-12 students and teachers' understanding of social, cultural, and ethical implications of AI [ 2]. Most studies reflect university students' engagement with ethical ideas about algorithmic bias, but few addresses how to promote students' understanding of AI and ethics in K-12 settings. Therefore, this article: (a) synthesizes ethical issues surrounding AI in education as identified in the educational literature, (b) reflects on different approaches and curriculum materials available for teaching students about AI and ethics (i.e., featuring materials from the MIT Media Lab and Code.org), and (c) articulates future directions for research and recommendations for practitioners seeking to navigate AI and ethics in K-12 settings.

Next, we briefly define the notion of artificial intelligence (AI) and its applications through machine-learning and algorithm systems. As educational and educational technology scholars working in the United States, and at the risk of oversimplifying, we provide only a brief definition of AI below, and recognize that definitions of AI are complex, multidimensional, and contested in the literature [ 9, 16, 38]; an in-depth discussion of these complexities, however, is beyond the scope of this paper. Second, we describe in more detail five applications of AI in education, outlining their potential benefits for educators and students. Third, we describe the ethical challenges they raise by posing the question: "how and in what ways do algorithms manipulate us?" Fourth, we explain how to support students' learning about AI and ethics through different curriculum materials and teaching practices in K-12 settings. Our goal here is to provide strategies for practitioners to reap the benefits while navigating the ethical challenges. We acknowledge that in centering this work within U.S. education, we highlight certain ethical issues that educators in other parts of the world may see as less prominent. For example, the European Union (EU) has highlighted ethical concerns and implications of AI, emphasized privacy protection, surveillance, and non-discrimination as primary areas of interest, and provided guidelines on how trustworthy AI should be [ 3, 15, 23]. Finally, we reflect on future directions for educational and other research that could support K-12 teachers and students in reaping the benefits while mitigating the drawbacks of AI in education.

2 Definition and applications of artificial intelligence

The pursuit of creating intelligent machines that replicate human behavior has accelerated with the realization of artificial intelligence. With the latest advancements in computer science, a proliferation of definitions and explanations of what counts as AI systems has emerged. For instance, AI has been defined as "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings" [ 49]. This particular definition highlights the mimicry of human behavior and consciousness. Furthermore, AI has been defined as "the combination of cognitive automation, machine learning, reasoning, hypothesis generation and analysis, natural language processing, and intentional algorithm mutation producing insights and analytics at or above human capability" [ 31]. This definition incorporates the different sub-fields of AI together and underlines their function while reaching at or above human capability.

Combining these definitions, artificial intelligence can be described as the technology that builds systems to think and act like humans with the ability of achieving goals. AI is mainly known through different applications and advanced computer programs, such as recommender systems (e.g., YouTube, Netflix), personal assistants (e.g., Apple's Siri), facial recognition systems (e.g., Facebook's face detection in photographs), and learning apps (e.g., Duolingo) [ 32]. To build on these programs, different sub-fields of AI have been used in a diverse range of applications. Evolutionary algorithms and machine learning are most relevant to AI in K-12 education.

2.1 Algorithms

Algorithms are the core elements of AI. The history of AI is closely connected to the development of sophisticated and evolutionary algorithms. An algorithm is a set of rules or instructions that is to be followed by computers in problem-solving operations to achieve an intended end goal. In essence, all computer programs are algorithms. They involve thousands of lines of codes which represent mathematical instructions that the computer follows to solve the intended problems (e.g., as computing numerical calculation, processing an image, and grammar-checking in an essay). AI algorithms are applied to fields that we might think of as essentially human behavior-such as speech and face recognition, visual perception, learning, and decision-making and learning. In that way, algorithms can provide instructions for almost any AI system and application we can conceive [ 27].

2.2 Machine learning

3 Benefits of AI applications in education

Personalized learning systems, automated assessments, facial recognition systems, chatbots (social media sites), and predictive analytics tools are being deployed increasingly in K-12 educational settings; they are powered by machine-learning systems and algorithms [ 29]. These applications of AI have shown promise to support teachers and students in various ways: (a) providing instruction in mixed-ability classrooms, (b) providing students with detailed and timely feedback on their writing products, (c) freeing teachers from the burden of possessing all knowledge and giving them more room to support their students while they are observing, discussing, and gathering information in their collaborative knowledge-building processes [ 26, 50]. Below, we outline benefits of each of these educational applications in the K-12 setting before turning to a synthesis of their ethical challenges and drawbacks.

3.1 Personalized learning systems

3.2 Automated assessment systems

Automated assessment systems are becoming one of the most prominent and promising applications of machine learning in K-12 education [ 42]. These scoring algorithm systems are being developed to meet the need for scoring students' writing, exams and assignments, and tasks usually performed by the teacher. Assessment algorithms can provide course support and management tools to lessen teachers' workload, as well as extend their capacity and productivity. Ideally, these systems can provide levels of support to students, as their essays can be graded quickly [ 55]. Providers of the biggest open online courses such as Coursera and EdX have integrated automated scoring engines into their learning platforms to assess the writings of hundreds of students [ 42]. On the other hand, a tool called "Gradescope" has been used by over 500 universities to develop and streamline scoring and assessment [ 12]. By flagging the wrong answers and marking the correct ones, the tool supports instructors by eliminating their manual grading time and effort. Thus, automated assessment systems deal very differently with marking and giving feedback to essays compared to numeric assessments which analyze right or wrong answers on the test. Overall, these scoring systems have the potential to deal with the complexities of the teaching context and support students' learning process by providing them with feedback and guidance to improve and revise their writing.

3.3 Facial recognition systems and predictive analytics

Facial recognition software is used to capture and monitor students' facial expressions. These systems provide insights about students' behaviors during learning processes and allow teachers to take action or intervene, which, in turn, helps teachers develop learner-centered practices and increase student's engagement [ 55]. Predictive analytics algorithm systems are mainly used to identify and detect patterns about learners based on statistical analysis. For example, these analytics can be used to detect university students who are at risk of failing or not completing a course. Through these identifications, instructors can intervene and get students the help they need [ 55].

3.4 Social networking sites and chatbots

Social networking sites (SNSs) connect students and teachers through social media outlets. Researchers have emphasized the importance of using SNSs (such as Facebook) to expand learning opportunities beyond the classroom, monitor students' well-being, and deepen student-teacher relations [ 5]. Different scholars have examined the role of social media in education, describing its impact on student and teacher learning and scholarly communication [ 6]. They point out that the integration of social media can foster students' active learning, collaboration skills, and connections with communities beyond the classroom [ 6]. Chatbots also take place in social media outlets through different AI systems [ 21]. They are also known as dialogue systems or conversational agents [ 26, 52]. Chatbots are helpful in terms of their ability to respond naturally with a conversational tone. For instance, a text-based chatbot system called "Pounce" was used at Georgia State University to help students through the registration and admission process, as well as financial aid and other administrative tasks [ 7].

In summary, applications of AI can positively impact students' and teachers' educational experiences and help them address instructional challenges and concerns. On the other hand, AI cannot be a substitute for human interaction [ 22, 47]. Students have a wide range of learning styles and needs. Although AI can be a time-saving and cognitive aide for teachers, it is but one tool in the teachers' toolkit. Therefore, it is critical for teachers and students to understand the limits, potential risks, and ethical drawbacks of AI applications in education if they are to reap the benefits of AI and minimize the costs [ 11].

4 Ethical concerns and potential risks of AI applications in education

The ethical challenges and risks posed by AI systems seemingly run counter to marketing efforts that present algorithms to the public as if they are objective and value-neutral tools. In essence, algorithms reflect the values of their builders who hold positions of power [ 26]. Whenever people create algorithms, they also create a set of data that represent society's historical and systemic biases, which ultimately transform into algorithmic bias. Even though the bias is embedded into the algorithmic model with no explicit intention, we can see various gender and racial biases in different AI-based platforms [ 54].

Considering the different forms of bias and ethical challenges of AI applications in K-12 settings, we will focus on problems of privacy, surveillance, autonomy, bias, and discrimination (see Fig. 1). However, it is important to acknowledge that educators will have different ethical concerns and challenges depending on their students' grade and age of development. Where strategies and resources are recommended, we indicate the age and/or grade level of student(s) they are targeting (Fig. 2).

Potential ethical and societal risks of AI applications in education

Student work from the activity of "Youtube Redesign" (MIT Media Lab, AI and Ethics Curriculum, p.1, [ 45])

One of the biggest ethical issues surrounding the use of AI in K-12 education relates to the privacy concerns of students and teachers [ 47, 49, 54]. Privacy violations mainly occur as people expose an excessive amount of personal information in online platforms. Although existing legislation and standards exist to protect sensitive personal data, AI-based tech companies' violations with respect to data access and security increase people's privacy concerns [ 42, 54]. To address these concerns, AI systems ask for users' consent to access their personal data. Although consent requests are designed to be protective measures and to help alleviate privacy concerns, many individuals give their consent without knowing or considering the extent of the information (metadata) they are sharing, such as the language spoken, racial identity, biographical data, and location [ 49]. Such uninformed sharing in effect undermines human agency and privacy. In other words, people's agency diminishes as AI systems reduce introspective and independent thought [ 55]. Relatedly, scholars have raised the ethical issue of forcing students and parents to use these algorithms as part of their education even if they explicitly agree to give up privacy [ 14, 48]. They really have no choice if these systems are required by public schools.

Another ethical concern surrounding the use of AI in K-12 education is surveillance or tracking systems which gather detailed information about the actions and preferences of students and teachers. Through algorithms and machine-learning models, AI tracking systems not only necessitate monitoring of activities but also determine the future preferences and actions of their users [ 47]. Surveillance mechanisms can be embedded into AI's predictive systems to foresee students' learning performances, strengths, weaknesses, and learning patterns. For instance, research suggests that teachers who use social networking sites (SNSs) for pedagogical purposes encounter a number of problems, such as concerns in relation to boundaries of privacy, friendship authority, as well as responsibility and availability [ 5]. While monitoring and patrolling students' actions might be considered part of a teacher's responsibility and a pedagogical tool to intervene in dangerous online cases (such as cyber-bullying or exposure to sexual content), such actions can also be seen as surveillance systems which are problematic in terms of threatening students' privacy. Monitoring and tracking students' online conversations and actions also may limit their participation in the learning event and make them feel unsafe to take ownership for their ideas. How can students feel secure and safe, if they know that AI systems are used for surveilling and policing their thoughts and actions? [ 49].

Problems also emerge when surveillance systems trigger issues related to autonomy, more specifically, the person's ability to act on her or his own interest and values. Predictive systems which are powered by algorithms jeopardize students and teachers' autonomy and their ability to govern their own life [ 46, 47]. Use of algorithms to make predictions about individuals' actions based on their information raise questions about fairness and self-freedom [ 19]. Therefore, the risks of predictive analysis also include the perpetuation of existing bias and prejudices of social discrimination and stratification [ 42].

Finally, bias and discrimination are critical concerns in debates of AI ethics in K-12 education [ 6]. In AI platforms, the existing power structures and biases are embedded into machine-learning models [ 6]. Gender bias is one of the most apparent forms of this problem, as the bias is revealed when students in language learning courses use AI to translate between a gender-specific language and one that is less-so. For example, while Google Translate translated the Turkish equivalent of "S he/he is a nurse" into the feminine form, it also translated the Turkish equivalent of " She/he is a doctor" into the masculine form [ 33]. This shows how AI models in language translation carry the societal biases and gender-specific stereotypes in the data [ 40]. Similarly, a number of problematic cases of racial bias are also associated with AI's facial recognition systems. Research shows that facial recognition software has improperly misidentified a number of African American and Latino American people as convicted felons [ 42].

Additionally, biased decision-making algorithms reveal themselves throughout AI applications in K-12 education: personalized learning, automated assessment, SNSs, and predictive systems in education. Although the main promise of machine-learning models is increased accuracy and objectivity, current incidents have revealed the contrary. For instance, England's A-level and GCSE secondary level examinations were cancelled due to the pandemic in the summer of 2020 [ 1, 57]. An alternative assessment method was implemented to determine the qualification grades of students. The grade standardization algorithm was produced by the regulator Ofqual. With the assessment of Ofqual's algorithm based on schools' previous examination results, thousands of students were shocked to receive unexpectedly low grades. Although a full discussion of the incident is beyond the scope of this article [ 51] it revealed how the score distribution favored students who attended private or independent schools, while students from underrepresented groups were hit hardest. Unfortunately, automated assessment algorithms have the potential to reconstruct unfair and inconsistent results by disrupting student's final scores and future careers [ 53].

5 Teaching and understanding AI and ethics in educational settings

5.1 "AI and Ethics Curriulum" for middle school students by MIT Media Lab

The MIT Media Lab team offers an open-access curriculum on AI and ethics for middle school students and teachers. Through a series of lesson plans and hand-on activities, teachers are guided to support students' learning of the technical terminology of AI systems as well as the ethical and societal implications of AI [ 2]. The curriculum includes various lessons tied to learning objectives. One of the main learning goals is to introduce students to basic components of AI through algorithms, datasets, and supervised machine-learning systems all while underlining the problem of algorithmic bias [ 45]. For instance, in the activity "AI Bingo", students are given bingo cards with various AI systems, such as online search engine, customer service bot, and weather app. Students work with their partners collaboratively on these AI systems. In their AI Bingo chart, students try to identify what prediction the selected AI system makes and what dataset it uses. In that way, they become more familiar with the notions of dataset and prediction in the context of AI systems [ 45].

In the second investigation, "Algorithms as Opinions", students think about algorithms as recipes, which are created by set of instructions that modify an input to produce an output [ 45]. Initially, students are asked to write an algorithm to make the "best" jelly sandwich and peanut butter. They explore what it means to be "best" and see how their opinions of best in their recipes are reflected in their algorithms. In this way, students are able to figure out that algorithms can have various motives and goals. Following this activity, students work on the "Ethical Matrix", building on the idea of the algorithms as opinions [ 45]. During this investigation, students first refer back to their developed algorithms through their "best" jelly sandwich and peanut butter. They discuss what counts as the "best" sandwich for themselves (most healthy, practical, delicious, etc.). Then, through their ethical matrix (chart), students identify different stakeholders (such as their parents, teacher, or doctor) who care about their peanut butter and jelly sandwich algorithm. In this way, the values and opinions of those stakeholders also are embedded in the algorithm. Students fill out an ethical matrix and look for where those values conflict or overlap with each other. This matrix is a great tool for students to recognize different stakeholders in a system or society and how they are able to build and utilize the values of the algorithms in an ethical matrix.

The final investigation which teaches about the biased nature of algorithms is "Learning and Algorithmic Bias" [ 45]. During the investigation, students think further about the concept of classification. Using Google's Teachable Machine tool [ 2], students explore the supervised machine-learning systems. Students train a cat-dog classifier using two different datasets. While the first dataset reflects the cats as the over-represented group, the second dataset indicates the equal and diverse representation between dogs and cats [ 2]. Using these datasets, students compare the accuracy between the classifiers and then discuss which dataset and outcome are fairer. This activity leads students into a discussion about the occurrence of bias in facial recognition algorithms and systems [ 2].

In the rest of the curriculum, similar to the AI Bingo investigation, students work with their partners to determine the various forms of AI systems in the YouTube platform (such as its recommender algorithm and advertisement matching algorithm). Through the investigation of "YouTube Redesign", students redesign YouTube's recommender system. They first identify stakeholders and their values in the system, and then use an ethical matrix to reflect on the goals of their YouTube's recommendation algorithm [ 45]. Finally, through the activity of "YouTube Socratic Seminar", students read an abridged version of Wall Street Journal article by participating in a Socratic seminar. The article was edited to shorten the text and to provide more accessible language for middle school students. They discuss which stakeholders were most influential or significant in proposing changes in the YouTube Kids app and whether or not technologies like auto play should ever exist. During their discussion, students engage with the questions of: "Which stakeholder is making the most change or has the most power?", "Have you ever seen an inappropriate piece of content on YouTube? What did you do?" [ 45].

Overall, the MIT Media Lab's AI and Ethics curriculum is a high quality, open-access resource with which teachers can introduce middle school students to the risks and ethical implications of AI systems. The investigations described above involve students in collaborative, critical thinking activities that force them to wrestle with issues of bias and discrimination in AI, as well as surveillance and autonomy through the predictive systems and algorithmic bias.

5.2 "AI and Data Privacy" workshop series for K-9 students by MIT Media Lab

Another quality resource from the MIT Media Lab's Personal Robots Group is a workshop series designed to teach students (between the ages 7 and 14) about data privacy and introduce them to designing and prototyping data privacy features. The group has made the content, materials, worksheets, and activities of the workshop series into an open-access online document, freely available to teachers [ 44].

The first workshop in the series is "Mystery YouTube Viewer: A lesson on Data Privacy". During the workshop, students engage with the question of what privacy and data mean [ 44]. They observe YouTube's home page from the perspective of a mystery user. Using the clues from the videos, students make predictions about what the characters in the videos might look like or where they might live. In a way, students imitate YouTube algorithms' prediction mode about the characters. Engaging with these questions and observations, students think further about why privacy and boundaries are important and how each algorithm will interpret us differently based on who creates the algorithm itself.

The second workshop in the series is "Designing ads with transparency: A creative workshop". Through this workshop, students are able to think further about the meaning, aim, and impact of advertising and the role of advertisements in our lives [ 44]. Students collaboratively create an advertisement using an everyday object. The objective is to make the advertisement as "transparent" as possible. To do that, students learn about notions of malware and adware, as well as the components of YouTube advertisements (such as sponsored labels, logos, news sections, etc.). By the end of the workshop, students design their ads as a poster, and they share with their peers.

The final workshop in MIT's AI and data privacy series is "Designing Privacy in Social Media Platforms". This workshop is designed to teach students about YouTube, design, civics, and data privacy [ 44]. During the workshop, students create their own designs to solve one of the biggest challenges of the digital era: problems associated with online consent. The workshop allows students to learn more about the privacy laws and how they impact youth in terms of media consumption. Students consider YouTube within the lenses of the Children's Online Privacy Protections Rule (COPPA). In this way, students reflect on one of the components of the legislation: how might students get parental permission (or verifiable consent)?

Such workshop resources seem promising in helping educate students and teachers about the ethical challenges of AI in education. Specifically, social media such as YouTube are widely used as a teaching and learning tool within K-12 classrooms and beyond them, in students' everyday lives. These workshop resources may facilitate teachers' and students' knowledge of data privacy issues and support them in thinking further about how to protect privacy online. Moreover, educators seeking to implement such resources should consider engaging students in the larger question: who should own one's data? Teaching students the underlying reasons for laws and facilitating debate on the extent to which they are just or not could help get at this question.

5.3 Investigation of "AI for Oceans" by Code.org

A third recommended resource for K-12 educators trying to navigate the ethical challenges of AI with their students comes from Code.org, a nonprofit organization focused on expanding students' participation in computer science. Sponsored by Microsoft, Facebook, Amazon, Google, and other tech companies, Code.org aims to provide opportunities for K-12 students to learn about AI and machine-learning systems [ 20]. To support students (grades 3-12) in learning about AI, algorithms, machine learning, and bias, the organization offers an activity called "AI for Oceans", where students are able to train their machine-learning models.

The activity is provided as an open-access tutorial for teachers to help their students explore how to train, model and classify data, as well as to understand how human bias plays a role in machine-learning systems. During the activity, students first classify the objects as either "fish" or "not fish" in an attempt to remove trash from the ocean. Then, they expand their training data set by including other sea creatures that belong underwater. Throughout the activity, students are also able to watch and interact with a number of visuals and video tutorials. With the support of their teachers, they discuss machine learning, steps and influences of training data, as well as the formation and risks of biased data [ 20].

6 Future directions for research and teaching on AI and ethics

In this paper, we provided an overview of the possibilities and potential ethical and societal risks of AI integration in education. To help address these risks, we highlighted several instructional strategies and resources for practitioners seeking to integrate AI applications in K-12 education and/or instruct students about the ethical issues they pose. These instructional materials have the potential to help students and teachers reap the powerful benefits of AI while navigating ethical challenges especially related to privacy concerns and bias. Existing research on AI in education provides insight on supporting students' understanding and use of AI [ 2, 13]; however, research on how to develop K-12 teachers' instructional practices regarding AI and ethics is still in its infancy.

Moreover, current resources, as demonstrated above, mainly address privacy and bias-related ethical and societal concerns of AI. Conducting more exploratory and critical research on teachers' and students' surveillance and autonomy concerns will be important to designing future resources. In addition, curriculum developers and workshop designers might consider centering culturally relevant and responsive pedagogies (by focusing on students' funds of knowledge, family background, and cultural experiences) while creating instructional materials that address surveillance, privacy, autonomy, and bias. In such student-centered learning environments, students voice their own cultural and contextual experiences while trying to critique and disrupt existing power structures and cultivate their social awareness [ 24, 36].

Finally, as scholars in teacher education and educational technology, we believe that educating future generations of diverse citizens to participate in the ethical use and development of AI will require more professional development for K-12 teachers (both pre-service and in-service). For instance, through sustained professional learning sessions, teachers could engage with suggested curriculum resources and teaching strategies as well as build a community of practice where they can share and critically reflect on their experiences with other teachers. Further research on such reflective teaching practices and students' sense-making processes in relation to AI and ethics lessons will be essential to developing curriculum materials and pedagogies relevant to a broad base of educators and students.

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This work was supported by the Graduate School at Michigan State University, College of Education Summer Research Fellowship.

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Michigan State University, East Lansing, MI, USA

Selin Akgun & Christine Greenhow

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Correspondence to Selin Akgun.

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Akgun, S., Greenhow, C. Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics 2, 431-440 (2022). https://doi.org/10.1007/s43681-021-00096-7
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Keywords

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Artificial intelligence in education: Addressing ethical challenges in K-12 settings - AI and Ethics

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Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students' learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners' behaviors.

# data[].attributes.body
Chen, Lijia, Pingping Chen, and Zhijian Lin. "Artificial intelligence in education: A review." Ieee Access 8 (2020): 75264-75278.

# data[].attributes.body
Roll, Ido, and Ruth Wylie. "Evolution and revolution in artificial intelligence in education." International journal of artificial intelligence in education 26 (2016): 582-599.

# data[].attributes.body
Gocen, Ahmet, and Fatih Aydemir. "Artificial intelligence in education and schools." Research on Education and Media 12.1 (2020): 13-21.

# data[].attributes.body
Akgun, Selin, and Christine Greenhow. "Artificial intelligence in education: Addressing ethical challenges in K-12 settings." AI and Ethics 2.3 (2022): 431-440.

# data[].attributes.body
"...recent advances in artificial intelligence have brought us to a turning point in human history."

# data[].attributes.body
"AI has the potential to transform our world, and at this moment we have the opportunity to anticipate those changes and brace for their potential impacts."

# data[].attributes.body
"...just as AI has evolved from basic versions to more sophisticated iterations, its expansion necessitates a Humanics 2.0."

# data[].attributes.body
Artificial intellegence and augmented reality can be useful tools to help personalize and improve student learning programs. However, it is necessary for educators and developers of such progams to collaborate in order to solve ethical and technical issues.

# data[].attributes.body
"Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place."

# data[].attributes.body
"Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention."

# data[].attributes.body
"Collaboration between educators, researchers, and technology developers is crucial to overcome these challenges."

# data[].attributes.body
Artificial intellegence has recently become more availible to the public, which has caused some to raise concerns about the abilities of AI. By educating about the appropriate use and control of AI, humanity can avoid an unlikely "doomsday" situation.

# data[].attributes.body
"...since the arrival of ChatGPT almost a year ago, AI has captivated the public's attention and reignited discussions about how it could transform the world."

# data[].attributes.body
"[AI experts] have sounded the alarm more than once in recent months that AI poses a "risk of extinction" of the human race and have called for a moratorium on its development."

# data[].attributes.body
"...it's imperative for the K-12 system to prepare students to be successful in the age of AI."

# data[].attributes.body
By studying students' opinions regarding the use of artificial intellegence, educators and legistalors can optimize AI as a tool for learning.

# data[].attributes.body
"AI-powered educational platforms are more dynamic and productive, as they can be adapted to the preferences, learning styles and pace of each student, enabling personalized learning."

# data[].attributes.body
"Educational institutions are increasingly incorporating new technologies into their classrooms, such as artificial intelligence (AI)..."

# data[].attributes.body
"The aim of this study is to gather information that will help educators, legislators, and AI developers optimize AI’s role in education for increased student achievement by examining students’ attitudes toward the implementation of AI in personalized learning."

# data[].attributes.body
Artificial intellegence can be beneficial as an educational device in an array of ways, by both educators and students.

# data[].attributes.body
"New artificial intelligence (AI) tools are rapidly making it less challenging to break down the proverbial classroom walls and create more interactive and engaging learning experiences in schools."

# data[].attributes.body
"From simple idea generation to the use of AI chatbots for research to working with customized GPTs (generative pre-trained transformers) targeted at specific content areas, teachers are experimenting with AI to level-up learning."

# data[].attributes.body
"AI allows [students] to become active participants in their own learning, offering limitless paths to engage with data, information, and other students in ways that are beyond our current imagination."

# data[].attributes.body
Artificial intellegence can be used to personalize and improve curriculums, which helps students to comprehend material.

# data[].attributes.body
"Using [AI], instructors have been able to perform different administrative functions, such as reviewing and grading students' assignments more effectively and efficiently..."

# data[].attributes.body
"...because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students' needs..."

# data[].attributes.body
"...AI has had a major impact on education, particularly, on administration, instruction, and learning areas of the education sector or within the context of individual learning institutions."

# data[].attributes.body
Artificial intellegence can be implemented into the education in multiple ways, including collaboration with teachers, diversifying technologies, and embedding the AI into students' everyday lives.

# data[].attributes.body
"...ILEs should be better integrated – with formal and informal learning environments, with teachers and their practices, with cultural norms, with existing resources, and with our learners’ everyday lives and tasks."

# data[].attributes.body
"While a human tutor often works one-on-one, for a specific duration and in constrained spaces, interactive learning environments can be collaborative, omnipresent, and portable."

# data[].attributes.body
"...ILE have unique affordances that human tutors do not, and the next generation of systems should leverage those affordances to support learning anytime, anywhere, by anyone."

# data[].attributes.body
It is vital to examine and prepare for the possible effects of the future implementation of artificial intellegence into education systems to better serve students' needs.

# data[].attributes.body
"...many scholars in [the field of AI] believe

that the role of teachers, school and leaders in education will change..."

# data[].attributes.body
"The results show that schools and teachers will have new products, benefits and also face drawbacks with the arrival of AI in education.

# data[].attributes.body
"While participants generally seem to have positive perceptions towards AI, there are also certain drawbacks, especially highlighted by teachers and academicians,

regarding the future of teaching."

# data[].attributes.body
While the use of artificial intellegence in education has many benefits, there are also ethical and moral drawbacks that must be appropriately addressed.

# data[].attributes.body
"AI has a variety of educational applications, such as personalized learning platforms to promote students’ learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners’ behaviors."

# data[].attributes.body
"Despite the potential benefits of AI to support students’ learning experiences and teachers’ practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts."

# data[].attributes.body
"The ethical challenges of AI in education must be identified and introduced to teachers and students."

# data[].attributes.headline
How will the implementation of artificial intelligence revolutionize education and how should we as a society prepare for/adapt to it's effects?

# data[].attributes.subject
How will the implementation of artificial intelligence revolutionize education and how should we as a society prepare for/adapt to it's effects?

# data[].attributes.body
Origin: Created and authored by Amal Zouhri and Mostafa El Mallahi. Zouhri received the B.Sc, M.Sc and Ph.D. degrees in electrical engineering from the Faculty of Sciences. Mallahi received a B.Sc, M.Sc, and Ph. D.degrees in computer science from the Faculty of Sciences. Professor of computer sciences in the Dept. of Mathematics and Computer Sciences at Height Normal School. Article created 2024, published June 2024 by Sciendo in Germany.

Purpose: Exists to inform readers of the impact of artificial intelligence and augmented reality on education, specifically focusing on the benefits. Authors created this piece to analyze the results of experiments on AI and AR in educational environments. Authors intend for the audience to consider integrating Artificial Intelligence into education to improve students’ experiences. Format (journal article) is more easily comprehensible and concise, which holds readers’ attention more effectively. Intended audience is mainly those who are interested in the implementation of new technologies like AI and AR into education, including educators, developers, and students. Authors most likely expected the same audience to read the article. This article conveys the idea that the integration of technologies like artificial intelligence can improve student comprehension, engagement, and motivation.

Value: From this article, the authors don’t seem unfairly biased toward the implementation of AI into education as they do address concerns regarding its difficulty to employ. At the time this piece was written, artificial intelligence was being developed and integrated into classrooms. This piece reflects the events of the time. We can tell that the number of educators who are willing to use artificial intelligence in their teaching is increasing exponentially.

Limitations: This article does not seem to be unfairly biased toward either side of the argument around artificial intelligence in education because it includes information about the drawbacks of artificial intelligence. However, it seems to focus more on the benefits rather than the disadvantages of using artificial intelligence in classrooms. This article was written during a crucial time in the development of artificial intelligence and it seems to accurately address the known information.

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Origin: Authored by Lauraine Langreo. Records of higher education are difficult to find. Journalist, currently a writer at Education Week. Focuses work on education technology, learning environments, future of work and student wellness issues. Article created 2023. Published 9/13/2023 by Editorial Projects in Education Inc. as an article.

Purpose: This piece exists to provide information about the role of artificial intelligence in society and how we can prepare future generations for its use. The author created this piece to address concerns about “doomsday” situations that could arise due to AI and propose systems to avoid them. The author intended to persuade the audience that change is necessary to prepare for a future with heavy use of artificial intelligence. Formatted to hold audience attention with interesting language and relatively casual writing style. Intended audience consists mostly of educators, people with children, and students. Author most likely thought people interested in artificial intelligence in education would consume this article. This piece conveys the idea that in order to avoid unlikely but still possible “doomsday" situations, we must adjust our education systems to prioritize educating students about artificial intelligence.

Value: We can tell that the author is aware of the change that artificial intelligence will bring to both education and daily life and that we must prepare accordingly. This article was written during the development of artificial intelligence and as AI was becoming more available to the general population. It reflects the limited knowledge held by society about the effects of artificial intelligence. We can tell that some people are concerned about the effects that artificial intelligence will have.

Limitations: This author is not apparently biased toward either side of the argument surrounding artificial intelligence. Most issues regarding artificial intelligence that are often discussed are addressed in this article. At the time this piece was written, artificial intelligence was an even newer technology than it is today. Considering the novelty of artificial intelligence at the time it was created, this article addressed most of the known, relevant information accurately and appropriately.

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Origin: Created/authored by Radoslav Baltezarević and Ivana Baltezarević. Radoslav Baltezarević received his master’s degree in 2008 at the Faculty of Business Studies and graduated at the Faculty of Management Zaječar in 2004. He is currently a full professor and senior research fellow at the institute of international politics and economics. He has a large amount of experience with communication, media studies, academic writing, new media technology, etc. Ivana Baltezarević obtained her PhD in Communication from the Faculty of Culture and Media in Belgrade, Republic of Serbia. She is currently an associate professor in Communication at Megatrend University. Article created 2024, published in Serbia by the International Journal of Cognitive Research in Science, Engineering & Education in 2024.

Purpose: This study was conducted to examine students’ attitudes toward artificial intelligence and the effects of similar “contemporary technologies” in education. The authors created the study to discuss and share results of the study regarding the use of artificial intelligence in education. The authors intended to inform the audience of valuable information which could possibly be used to improve student experiences and efficiency. This study is presented with an abstract, an introduction, a discussion, and a conclusion. This allows the audience to better comprehend the findings. The intended audience and who the authors most likely expected to consume this article is educators, students, and technological developers. This piece creates the impression that students generally appear to have positive opinions on new technologies in education.

Value: The authors are simply examining objective evidence, so no personal opinions are very clearly distinguishable. This piece was written recently, but still reflects the current lack of knowledge regarding artificial intelligence as a technology. We can tell from this article that students generally have a positive attitude toward artificial intelligence.

Limitations: There is no apparent bias shown by the authors in this article as they are analyzing objective evidence with reasoning. As with any study, there is a limited sample size, so information will most likely not be completely accurate/reflect the truth. While this piece was written, artificial intelligence was still being developed and implemented as a tool for education. This article seems to address all relevant information accurately and fairly.

# data[].attributes.body
Origin: This piece was created/authored by Amy Holcombe and Steve “Woz” Wozniak. Holcombe obtained a Phd in curriculum and teaching from UNCG, a masters in school administration from NC A&T, an M.Ed. in english from UNCG, and a B.A. in english journalism from Sonoma State University. She currently occupies the position of Dean at Stout School of Education. Wozniak received a degree in Electrical Engineering and Computer Science from UC Berkeley. Steve Wozniak occupied many important positions. He is the co-founder of Apple Computers Inc. The date of creation and publication is unknown. However, it must be fairly recent considering the recent development of artificial intelligence. Published by the Association for Supervision and Curriculum Development in the U.S.

Purpose: This piece exists to argue that artificial intelligence can be a useful tool to increase student enjoyment and comprehension. The authors created this piece to inform the audience of the uses and benefits of artificial intelligence in education and classrooms. It is formatted as a relatively short article with multiple sections each addressing different possible uses of artificial intelligence in a classroom setting. The authors most likely expected and intended for educators and those with children to read this piece. This piece creates the idea that artificial intelligence is a revolutionary technology that will allow students to be more involved and benefit more greatly from their education.

Value: We can tell that the authors are in support of the use of artificial intelligence in education and that they believe students will find learning more enjoyable with the employment of such technologies. This piece was most likely written at a time when artificial intelligence was still being developed. We can tell that educators have been considering using artificial intelligence more often as an assistive technology for education.

Limitations: The authors seem biased toward the use of artificial intelligence in education. Negative effects of artificial intelligence are not discussed in depth in this article, and consequences of or alternatives for artificial intelligence in education are also excluded. While this article does not address many concerns regarding the possible unintended consequences of using artificial intelligence in classroom settings, it does address all other information accurately.

# data[].attributes.body
Origin: Created/authored by Lijia Chen, Pingping Chen, and Zhijian Lin. Lijia Chen received a B.S. degree and an M.S. degree in fine arts and designs from Jiangxi Normal University. She is currently an Assistant Professor with Yango University. Pingping Chen received a PhD in electronic engineering from Xiamen University. He is currently a professor at Fuzhou University.  Zhijian Lin received his B.S., M.S., and PhD degrees in communication engineering from Xiamen University. Article created 2020, published 2020 by IEEE.

Purpose: This piece exists to share the findings of a study on the impact of artificial intelligence in education. The authors created this piece to analyze and discuss the results of this study and suggest applications of artificial intelligence in the field of education. The authors intended the audience to consider implementing artificial intelligence into their lives, specifically in education. The format is a scientific analysis of data, so it appeals mostly to logos. The authors most likely intended for and expected educators, students, and artificial intelligence developers to read this article. This piece conveys the idea that artificial intelligence can be adopted by educators as an administrative assistant and as a helpful tool in student learning.

Value: We can tell that the authors believe that artificial intelligence has had and will continue to have a major impact on education. This piece was written as artificial intelligence was emerging as a more commonly used tool by educators. It reflects the considerable effect that artificial intelligence was creating as it was being developed. We can tell from this piece that artificial intelligence has already had an extensive impact on education, whether overall positive or negative.

Limitations: The authors do not appear to be unfairly biased toward either side of the argument surrounding the use of artificial intelligence in education as they are only analyzing objective scientific data. Negative effects and/or unwanted drawbacks of using artificial intelligence in education are not addressed in depth. While this piece was written there was not as much information regarding artificial intelligence as there is today. This article appears to address all information relevant to the study accurately and analyze the findings of the research properly.

# data[].attributes.body
Origin: Created/authored by Ido Roll and Ruth Wylie. Roll studied at The Hebrew University of Jerusalum and received his PhD in Human-Computer Interaction from Carnegie Mellon University. He is an associate professor at the Faculty of Data and Decision Sciences. He is also affiliated with the Faculty of Education in Science and Technology and has held a position at the University of British Columbia. Wylie earned a master’s degree and PhD in Human-Computer Interaction at Carnegie Mellon University. She received a bachelor’s degree in Cognitive Science at the University of California, Berkeley, with minors in Computer Science and Education. She is co-director and research associate professor at the Center for Science and the Imagination at Arizona State University. This article was published February 22nd, 2016.

Purpose: This piece exists to inform the reader of the benefits of AIED (or “Artificial Intelligence in Education) as a tool for learning. The authors created this piece to analyze the findings of “paper” tests regarding the effects of AIED. The authors intend for the readers to consider the positive effects of AIED on student learning, comprehension, and engagement. This is formatted as an analysis of a study, which provides an abstract and concluding remarks to summarize the text for readers. This piece is mostly targeted toward educators who might be considering using artificial intelligence in their classrooms. The authors most likely thought that the main consumers of this piece would be educators and those interested in AIED. This piece conveys the idea that artificial intelligence is beginning to replace human tutors and has great potential as an assistive technology in education.

Value: We can tell from this piece that the authors believe that artificial intelligence is a revolutionary technology that will greatly affect and benefit those who take advantage of it in educational settings. At the time this piece was written, artificial intelligence was still being developed. This piece reflects the lack of knowledge about this subject, specifically the lack of knowledge about artificial intelligence’s negative repercussions. We can tell from this piece that artificial intelligence is still a relatively novel technology but that it has the potential to help students in their learning.

Limitations: The authors seem biased toward the use of AIED. They do not, in detail, address the possible unwanted/negative effects of artificial intelligence in education. While this piece was being written, artificial intelligence was still being developed as a technology, and was beginning to be used more often in schools. It seems that information about the repercussions of using AIED are addressed.

# data[].attributes.body
Origin: Created and authored by Ahmet Göçen and Fatih Aydemir. Göçen has a PhD in Education Management and currently works as an associate professor at Afyon Kocatepe University and a coordinator and researcher at EU projects. Aydemir currently is affiliated with Gaziosmanpasa University in Türkiye. This article was published in 2020 by Sciendo as part of a volume in research on education and media.

Purpose: This piece exists to share the findings of a study on the different topics within the use of artificial intelligence in education. The authors created this piece to inform the audience of the viewpoints of people who are to be affected by the use of artificial intelligence in education. They intend for the audience to reconsider their viewpoints on artificial intelligence in education. The authors intend this piece to reach those who have the power to implement artificial intelligence in educational environments. The authors most likely thought that the same group would read this piece in reality. This piece conveys the idea that artificial intelligence should be used as a tool for learning but that we must also be aware of the possible negative effects that using artificial intelligence in education could have once adopted.

Value: We can tell from this piece that the authors are willing to consider all viewpoints regarding the use of artificial intelligence in education. This piece was written during a time when artificial intelligence was being developed and in the early stages of being regularly found in classroom settings. This piece accurately reflects the concerns held by many, especially at the time at which this was written, about artificial intelligence in education. We can tell from this piece that although artificial intelligence can be a very useful tool in education, it can also have negative repercussions that are unintended.

Limitations: The authors appear unbiased in this piece. They do not purposefully exclude any information to benefit any viewpoint regarding artificial intelligence in education. While this piece was written, artificial intelligence was still being developed as a technology and especially as a tool for learning and an assistive technology in education. This piece seems to accurately and appropriately address all relevant information gathered in the study objectively.

# data[].attributes.body
Origin: Created/authored by Selin Akgun and Christine Greenhow. Akgun is a PhD student at Michigan State University who is working as a research assistant in the Multiple Literacies in Project-Based Learning project at CREATE for STEM institute. Greenhow received an Ed.D from Harvard University and works as a professor of educational psychology and educational technology at Michigan State University. This article was published September 22nd, 2021 in “AI and Ethics.”

Purpose: This piece exists to better inform the audience of the effects and uses of artificial intelligence in education. The authors created this piece to share their opinions regarding both the positive and negative effects of artificial intelligence in education. The authors intend for the audience to better understand the possible effects of artificial intelligence in education, both beneficial and harmful. This piece is formatted as an article or a section of a textbook. Both the intended audience and who the authors most likely thought would consume this piece is the group of people interested in artificial intelligence in education. This piece conveys the idea that artificial intelligence can be used in many ways as a helpful tool in education.

Value: We can tell from this piece that the authors are aware of multiple viewpoints regarding the use of artificial intelligence in education. At the time this piece was written, artificial intelligence was slowly being implemented into classrooms and was beginning to be used as an assistive technology in education. This piece accurately reflects the rising concerns about artificial intelligence that were emerging at the time. We can tell from this piece that the use of artificial intelligence can be a useful tool but also that many are concerned about the negative, unwanted repercussions.

Limitations: The authors of this article do not appear to be biased. This article does address concerns about artificial intelligence in education while also listing benefits and uses. While this piece was being written, artificial intelligence was still a relatively novel technology. The authors address all relevant information accurately and seemingly objectively.

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Origin: Created and authored by Joseph E. Aoun. Masters in oriental languages and literature, diploma of advanced studies in general and theoretical linguistics, Phd in linguistics. President of Northeastern University. Article created 2024, published Washington D.C. by Chronicle of Higher Education 8/2/2024.

Purpose: Exists to address concerns regarding the implementation of artificial intelligence into education. Author most likely created this piece of work to voice his opinions on preparing students for AI in education. Author intends for the audience to be aware of artificial intelligence and prepare for its use. Format (newspaper article) is more accessible to many people. Intended audience is mostly students and educators who could be affected by AI. Author most likely thought that the regular readers of the newspaper would read this. This document conveys the idea that artificial intelligence will have beneficial effects on education but must be prepared to address concerns/corresponding issues.

Value: From this piece, we can tell that the author does not have strong opinions regarding AI and is aware of all sides of the controversy surrounding it. At the time this article was written, artificial intelligence was being implemented into education. This piece reflects the differing opinions regarding this and the lack of knowledge about this topic. We can tell that there are many differing opinions on the subject of artificial intelligence, most of them with sufficient evidence and reasoning.

Limitations: This article doesn’t appear strongly biased toward any viewpoint of the argument surrounding AI. However, it seems slightly biased toward the use of AI in education only with proper training and appropriate use. This document focuses mainly on how we can prepare for AI in education, so other solutions are not discussed in detail. This piece was written during an important time period in the development and implementation of AI. This piece seems accurate to what is known. The author does not discuss alternate solutions for issues with AI. It seems that all information relevant to the claim is addressed and accurate.