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Few technologies have destabilized higher education like generative AI. Since ChatGPT's debut, educators have been grappling with what these tools mean for them, for their students, and for teaching overall. While some educators have embraced these tools to enhance their teaching, many are left wondering how their students are actually using AI and whether it's helping or hindering their learning.

To help educators better understand how students are experimenting with generative AI tools, we asked them. Some students we talked to are AI evangelists who've committed time and energy to using the technology; others feel more hesitant about it. But all have thought deeply about whether and how generative AI can be used effectively in education.

As you review their answers, consider whether you should adjust your thinking about AI's place in the classroom and how you might guide your own students in using these tools productively.

Q. How do you use generative AI in your day-to-day studies?

Q. What do you believe are generative AI's greatest strengths and weaknesses in relation to education?

Ravikumar: Generative AI's greatest strengths are its abilities to answer unlimited questions and give personalized responses. Its greatest weaknesses are the chance that it may hallucinate and give incorrect responses, which interferes with the learning process, as well as its current inability to perform well at creative tasks.

In my experience, ChatGPT gives generic and cliché responses in most creative applications. For example, when my marketing professor recommended using ChatGPT to brainstorm creative solutions to a business problem, I primarily received repetitive, generic solutions from the AI, even after multiple rounds of prompting from different angles. The burden of thinking creatively about the problem still fell on me (the human).

"In a regular classroom, a teacher can rarely give individualized attention to every student. With generative AI, every student has a personal tutor in their pocket."

Ramya Nambala

Bashkansky: Its greatest strength is that it can be a patient and extremely knowledgeable tutor. The most concerning weakness is that it's far too easy to get LLMs to do your homework for you.

Jurkovic: I find current AI systems to be quite useful for help with programming, as well as dealing with large amounts of data. In terms of weaknesses, one of their current problems is that they often hallucinate, and they can't really work for long periods of time without getting lost or confused. However, I think these weaknesses are temporary engineering problems. The rate of progress is very high, so I expect them to be solved within four years.

Nambala: The major strengths are, first, that it can speed up your progress by consolidating class information and, second, that it can personalize your learning and meet you where you're at. In a regular classroom, a teacher can rarely give individualized attention to every student. With generative AI, every student has a personal tutor in their pocket. AI's major weakness is that it hallucinates. ChatGPT, for example, is trained on human language, but it's not trained for accuracy. So, sometimes, it'll be inaccurate, and you cannot tell when.

Rosa: I think the greatest weakness of generative AI is that it is not yet regulated and not yet well understood. There are so many ways that students can use generative AI unethically, including using it to write their assignments for them, and if the AI is sophisticated enough, the teacher may never know. The strength, on the other hand, is that teachers and students can use generative AI for menial tasks, like proofreading texts, and this allows for more time for creative thinking.

Q. Do you have any apprehensions or concerns about generative AI's place in education?

Jurkovic: I think that current educational institutions are not fit to meet the rate of change in AI and are lagging in reacting to it appropriately. Using AI effectively will become a crucial skill in the job market, and we're not being prepared for that kind of world.

Nambala: I do have some apprehensions about generative AI's place in the classroom, but I'm more apprehensive about some of the outright bans of technology I've seen in classrooms. When we ban AI, we're banning a resource that students could use, and we're delaying the inevitable, which is that humans are now going to be using AI consistently.

Also, one of the major problems I'm seeing is that there's a huge gap between students' knowledge and teachers' knowledge of this new technology. So, if students cheat with this technology by using it to write their assignments, teachers may not be aware of what they're doing or the extent to which they may be doing it. I've worked with many high school students through my AI-focused nonprofit Athena EdTech, and these student ambassadors have reported that their peers often misuse AI in this way. However, teachers and administrators are learning to use AI checkers and school regulations to successfully lessen instances of cheating.

"I don't use generative AI for my studies. I don't trust where the information is coming from, so I can't be confident in its outputs."

Annemarie Rosa

Ravikumar: While I think generative AI should become ubiquitous in supplementing learning methods in education, I am apprehensive about its differential rates of adoption among different demographics and countries, which might affect their overall savviness with this powerful technology. I am also apprehensive about generative AI's tendency to hallucinate and teach students concepts incorrectly when they are early on in their learning journey.

Rosa: My biggest apprehension relates to the weakness I stated above: Many teachers lack clear rules on how to use it or an understanding of how to stop unethical use. If students can use it to write their homework without teachers knowing, they're both missing out on learning experiences and potentially plagiarizing. Since generative AI can pull from anywhere, students who use it may end up accidentally using someone else's work without credit. And if teachers can't recognize assignments written with generative AI, they may never know. Furthermore, students and their teachers can't always be certain that research done with generative AI is accurate because, again, AI can pull from anywhere.

Q. What advice would you give to educators and school administrators regarding their approach to generative AI?

Rosa: One piece of advice is to become knowledgeable in generative AI yourself. If you know the technology, you can better spot when students use it to do their work for them. And you can help students understand the beneficial ways to use it and teach them how to do so effectively.

Bashkansky: It's not just that AI is here to stay; AI will only get better. There's nothing stopping us from having a cheap LLM developed in the next few years that can pass any Harvard class with an A. Structure your courses accordingly-not by banning generative AI, but by embracing it. Teach courses on prompting. Teach courses on the ethics of AI, on its expected economic impacts, on how to use LLMs in the context of English essays. Become an expert on generative AI-yes, even if your background has nothing to do with computer science. React according to what generative AI will look like in three years, not what it looked like three years ago.

"I have noticed that since the introduction of generative AI, instructors have made their courses, assignments, and exams more focused on testing analytical and critical thinking abilities while applying core concepts, instead of relying on rote knowledge, which I think is a shift for the better."

Anjali Bathra Ravikumar

Nambala: At this point, we can't control that AI is in the world; it's a Pandora's box that we can't take back. The only thing we can do is lean into this change, embrace the upsides and mitigate the downsides. By creating bans that don't allow students to use AI at all, you're simply delaying the inevitable.

Ravikumar: I would recommend that professors don't shy away from encouraging students to use generative AI in a constructive manner. It is important to drive its adoption among students and set the appropriate guardrails necessary to ensure optimal learning outcomes. Moreover, I have noticed that since the introduction of generative AI, instructors have made their courses, assignments, and exams more focused on testing analytical and critical thinking abilities while applying core concepts, instead of relying on rote knowledge, which I think is a shift for the better.

Finding generative AI's place in the classroom

While every student has a different approach to generative AI, for many, including those featured here, that approach is built around the goal of becoming better learners. Despite some lingering concerns regarding AI's place in higher education, many of these students have high hopes for its potential to enhance education and better prepare students for their futures.

One key theme emerges from all students' answers: generative AI is here to stay. Educators have a responsibility to learn about the technology, experiment with it, and develop a classroom policy built on an informed understanding of how it may shape the future.

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How Students Are Actually Using Generative AI

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We polled several higher ed students to find out how they are experimenting with generative AI tools like ChatGPT for learning. Discover what they think about the benefits, challenges, and diverse ways AI is reshaping the classroom.

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Popular:

ttps://www.nature.com/articles/d41586-024-02371-z

Academic:

https://www.jstor.org/stable/community.37312334?searchText=College+student+ai+usage&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3DCollege%2Bstudent%2Bai%2Busage%26so%3Drel&ab_segments=0%2Fbasic_search_gsv2%2Fcontrol&refreqid=fastly-default%3A01096c19075b188811ed3d0e42991822&seq=1

Bonus: https://www.carleton.edu/writing/plagiarism/plagiarism-and-ai/#:~:text=At%20what%20point%20does%20a,all%20instructors%20would%20agree%20on.

I used Jstor for my academic resource and Google to find my popular one. The bonus one, I feel - is a bridge between popular and academic in its brevity but not overrun with ads and fun images. This I also found using google.

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Can you spot the bot? Identifying AI-generated writing in college essays.

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OpenAI Courts Trump With Vision for ‘A.I. in America’

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Talking about what students thinking about generative AI.

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AI Can’t Replace Teaching, but It Can Make It Better

https://www.wired.com/story/what-aspects-of-teaching-should-remain-human/

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AI Can’t Replace Teaching, but It Can Make It Better

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Even techno-optimists hesitate to say teaching is best left to the bots, but there’s a debate about where to draw the line.

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Are AI detection and plagiarism similarity scores worthwhile in the age of ChatGPT and other Generative AI?

Scu EBSCOhost

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Are AI detection and plagiarism similarity scores worthwhile in the age of ChatGPT and other Generative AI? | EBSCOhost

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Discover this 2024 paper in SOTL in the South by Baron, Philip

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ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning

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ChatGPT and GlobalHigher Education: UsingArtificial Intelligence inTeaching and Learning

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One of the most intense discussions taking place among university faculty is whether to permit students to use artificial intelligence in the classroom. To gain perspective on the matter, The Conversation reached out to four scholars for their take on AI as a learning tool and the reasons why they will or won't be making it a part of their classes.
Nicholas Tampio, professor of political science: Learn to think for yourself

As a professor, I believe the purpose of a college class is to teach students to think: to read scholarship, ask questions, formulate a thesis, collect and analyze data, draft an essay, take feedback from the instructor and other students, and write a final draft.

One problem with ChatGPT is that it allows students to produce a decent paper without thinking or writing for themselves.

In my American political thought class, I assign speeches by Martin Luther King Jr. and Malcolm X and ask students to compose an essay on what King and X might say about a current American political debate, such as the Supreme Court's recent decision on affirmative action.

Students could get fine grades if they used ChatGPT to "write" their papers. But they will have missed a chance to enter a dialogue with two profound thinkers about a topic that could reshape American higher education and society.

The point of learning to write is not simply intellectual self-discovery. My students go on to careers in journalism, law, science, academia and business. Their employers often ask them to research and write about a topic.

Few employers will likely hire someone to use large language models that rely on an algorithm scraping databases filled with errors and biases. Already, a lawyer has gotten in trouble for using ChatGPT to craft a motion filled with fabricated cases. Employees succeed when they can research a topic and write intelligently about it.

Artificial intelligence is a tool that defeats a purpose of a college education - to learn how to think, and write, for oneself.

Patricia A. Young, professor of education: ChatGPT doesn't promote advanced thinking

College students who are operating from a convenience or entitlement mentality - one in which they think, "I am entitled to use whatever technology is available to me" - will naturally gravitate toward using ChatGPT with or without their professor's permission. Using ChatGPT and submitting a course assignment as your own creation is called AI-assisted plagiarism.

Marlayna Demond for University of Maryland, Baltimore County

Some professors allow the use of ChatGPT as long as students cite ChatGPT as the source. As a researcher who specializes in the use of technology in education, I believe this practice needs to be thought through. Does this mean that ChatGPT would need to cite its sources, so that students could cite ChatGPT as a type of secondary source according to APA style, a standard academic style of citing papers? What Pandora's box are we opening? Some users report that ChatGPT never reveals its sources anyway.

The proliferation of free AI means students won't have to think much while writing - just engage in a high level of copy and paste. We used to call that plagiarism. With AI-assisted plagiarism, this brings in the potential for a new era of academic misconduct.

The concern will come when students take higher-level courses or land a job and lack the literacy skills to perform on an exceptional level. We will have created a generation of functionally illiterate adults who lack the capacity to engage in advanced thinking - like critiquing, comparing or contrasting information.

Yes, students can and should use smart tools, but we need to hypothesize and measure the costs to human ingenuity and the future of the human race.

Asim Ali, instructor of information systems management: AI is another teacher

I teach information systems management, and in the spring of 2023, I had students use ChatGPT for an essay assignment and then record a video podcast discussing how AI will impact their careers. This semester I am being more intentional by providing guidance on the possibilities and limitations of AI tools for each assignment. For example, students learn that using generative AI on a self-reflection assignment may not help, but using AI to analyze a case study is potentially a great way to find insights they may have overlooked. This emulates their future jobs in which they may use AI tools to enhance the quality of their work product.

My experience with adapting to AI for my own course inspired me to create a resource for all my colleagues. As executive director of the Biggio Center for the Enhancement of Teaching and Learning, I oversee the instructional design and educational development teams at Auburn University. We created a self-paced, online course called Teaching With AI.

Now there are over 600 faculty at Auburn and hundreds of faculty at almost 35 institutions engaging with the content and each other through discussion boards and practical exercises.

I receive messages from faculty sharing ways they are changing their assignments or discussing AI with their students. Some see AI as a threat to humans, but discussing AI with my students and with colleagues across the country has actually helped me develop human connections.

Shital Thekdi, associate professor of analytics & operations: What can you do that AI can't?

This semester, I will ask students in my Statistics for Business and Economics course to discuss the question, "What is your value beyond the AI tools?" I want them to reframe the conversation beyond one of academic integrity and instead as a challenge. I believe students must recognize that the jobs they imagine will exist for them could be eliminated because of these new technologies. So the pressure is on students to understand not only how to use these tools but also how to be better than the tools.

I hope my students will consider ethical reasoning and the role of human connections. While AI can be trained to make value-based decisions, individuals and groups have their own values that can differ considerably from those used by AI. And AI tools do not have the capacity to form human connections and experiences.

Students will remain vital contributors to business and society as AI tools develop. I believe it's our responsibility as educators to prepare our students for a rapidly evolving cultural and technological landscape.

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Should AI be permitted in college classrooms? 4 scholars weigh in

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Scholars differ over whether having students use AI in their assignments will help or hurt their careers after graduation.

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Generative AI Already Being Used in Majority of College Classrooms, According to Instructors in New Wiley Survey

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More than half say they or their students are using AI, and another third of those who aren’t would consider doing so in the future HOBOKEN, NJ— A new report from Wiley (NYSE: WLY) suggests that generative artificial intelligence (AI) is already being used in the majority of college classrooms—and that number could climb quickly. The majority (58%) of college instructors who responded to Wiley’s recent survey say they or their students are already using generative AI in their classrooms, accordi

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Editorial: Generative artificial intelligence as a plagiarism problem. | EBSCOhost

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Discover this 2023 paper in Biological Psychology by Dien, Joseph

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Generative AI’s impact on students of color and diverse students

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Let me be transparent here. Much of this Relevance Report entry uses generative AI — specifically ChatGPT. Generative AI is here to stay and its impact on our daily lives is everywhere. As the use of

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Exploring the integration and utilization of generative AI in formative e-assessments: A case study in higher education

Dongpeng Huang, Yixuan Huang, James J. Cummings

Boston University

https://research-ebsco-com.libproxy.scu.edu/linkprocessor/plink?id=82b9c3fb-dcac-3018-8b55-424f4b651367

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This one is very similar to the popular article that I chose, which is on "what do students think about and use generative AI"

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International Journal of Educational Technology in Higher Education 20, Article number: 43 (2023) Cite this article

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Abstract

This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs' 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students' perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students' perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.

Highlights

This study focuses on the integration of generative AI (GenAI) technologies, like ChatGPT, into higher education settings.

University students' perceptions of generative AI technologies in higher education were explored, including familiarity, potential benefits, and challenges.

A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning.

Insights from this study can inform policy development around the integration of GenAI technologies into higher education, helping to create well-informed guidelines and strategies for responsible and effective implementation.

Generative Artificial Intelligence

Generative AI (GenAI) encompasses a group of machine learning algorithms designed to generate new data samples that mimic existing datasets. One of the foundational techniques in GenAI is the Variational Autoencoder (VAE), which is a type of neural network that learns to encode and decode data in a way that maintains its essential features (Kingma & Welling, 2013). Another popular GenAI method is Generative Adversarial Networks (GANs), which consist of two neural networks working in competition to generate realistic data samples (Goodfellow et al., 2014). GenAI models use advanced algorithms to learn patterns and generate new content such as text, images, sounds, videos, and code. Some examples of GenAI tools include ChatGPT, Bard, Stable Diffusion, and Dall-E. Its ability to handle complex prompts and produce human-like output has led to research and interest into the integration of GenAI in various fields such as healthcare, medicine, education, media, and tourism.

ChatGPT, for example, has caused a surge of interest in the use of GenAI in higher education since its release in November 2022 (Hu, 2023). It is a conversational AI system developed by OpenAI, an autoregressive large language model (more than 175 billion parameters) has been pre-trained on a large corpus of text data. It can generate human-like responses to a wide range of text-based inputs. The model has been trained on a diverse range of texts, including books, articles, and websites, allowing it to understand user input, generate responses, and maintain coherent conversations on a wide range of topics. There has been much discussion on its potential in transforming disciplinary practices such as medical writing (Biswas, 2023; Kitamura, 2023), surgical practice (Bhattacharya et al., 2023), and health care communications (Eggmann et al., 2023) as well as enhancing higher education teaching and learning (e.g., Adiguzel et al., 2023; Baidoo-Anu & Ansah, 2023).

Benefits and challenges of using generative AI in higher education

One of the key uses of GenAI in higher education is for enhancing students' learning experience through its ability to respond to user prompts to generate highly original output. Text-to-text AI generators can provide writing assistance to students, especially non-native English-speaking students (Chan & Lee, 2023), by enabling them to brainstorm ideas and get feedback on their writing through applications such as ChatGPT (Atlas, 2023), while text-to-image AI generators such as DALL-E and Stable Diffusion can serve as valuable tools for teaching technical and artistic concepts in arts and design (Dehouche & Dehouche, 2023). GenAI tools are also believed to be useful research aids for generating ideas, synthesizing information, and summarising a vast amount of text data to help researchers analyse data and compose their writing (Berg, 2023; Chan & Zhou, 2023), contributing to efficiency in publication (Kitamura, 2023; van Dis et al., 2023). Another opportunity in which GenAI can bring benefits is learning assessment (Crompton & Burke, 2023). Tools such as the Intelligent Essay Assessor are used to grade students' written work and provide feedback on their performance (Landauer, 2003). Mizumoto and Eguchi ( 2023) examined the reliability and accuracy of ChatGPT as an automated essay scoring tool, and the results show that ChatGPT shortened the time needed for grading, ensured consistency in scoring, and was able to provide immediate scores and feedback on students' writing skills. Such research demonstrates that GenAI has potential to transform the teaching and learning process as well as improve student outcomes in higher education.

On the other hand, there have been challenges about the limitations of GenAI and issues related to ethics, plagiarism, and academic integrity. Kumar's ( 2023) analysis of AI-generated responses to academic writing prompts shows that the text output, although mostly original and relevant to the topics, contained inappropriate references and lacked personal perspectives that AI is generally incapable of producing. For second language learners, constructing appropriate prompts poses a challenge in itself as it requires a certain level of linguistic skills; and overreliance on GenAI tools may compromise students' genuine efforts to develop writing competence (Warschauer et al., 2023). In addition, the content produced by GenAI may be biased, inaccurate, or harmful if the dataset on which a model was trained contains such elements (Harrer, 2023). AI-generated images, for example, may contain nudity or obscenity and can be created for malicious purposes such as deepfakes (Maerten & Soydaner, 2023). GenAI tools are not able to assess validity of content and determine whether the output they generate contains falsehoods or misinformation, thus their use requires human oversight (Lubowitz, 2023). Furthermore, since AI-generated output cannot be detected by most plagiarism checkers, it is difficult to determine whether a given piece of writing is the author's original work (Peres et al., 2023). According to Chan ( 2023a), "it raises the question of what constitutes unethical behaviour in academic writing including plagiarism, attribution, copyrights, and authorship in the context of AI-generated content"-an AI-plagiarism. As Zhai ( 2022) cautions, the use of text-to-text generators such as ChatGPT may compromise the validity of assessment practices, particularly those involving written assignments. Hence, the widespread use of GenAI can pose a serious threat to academic integrity in higher education. In Chan and Tsi ( 2023) study, there is a particular concern towards holistic competency development such as creativity, critical thinking. The benefits of GenAI underline the potential of the technology as a valuable learning tool for students, while its limitations and challenges show a need for research into how GenAI can be effectively integrated in the teaching and learning process. Thus, the research questions for this study are

1.
How familiar are university students with GenAI technologies like ChatGPT?

2.
What are the potential benefits and challenges associated with using GenAI in teaching and learning, as perceived by university students?

3.
How can GenAI be effectively integrated into higher education to enhance teaching and learning outcomes?

Student perceptions of the use of GenAI in higher education

User acceptance is key to successful uptake of technological innovations (Davis, 1989). John Biggs emphasized the importance of student perception in his 3P (Presage-Process-Product) model of teaching and learning (Biggs, 2011). According to Biggs, students' perceptions of their learning environment, their abilities, and the teaching strategies used have a significant impact on their approach to learning (Biggs, 1999), which in turn influences their learning outcomes. Students who perceive the learning environment (such as, curriculum content, teaching methods, assessment methods, learning resources, learning context, student support services) positively and feel confident about their abilities are more likely to adopt a deep approach to learning, which involves seeking understanding and making connections between concepts. On the other hand, students who have a negative perception of their learning environment or doubt their abilities may adopt a surface approach to learning, where they focus on memorizing facts and meeting minimum requirements (Biggs, 2011). In a learning environment, the way students perceive a technological innovation such as GenAI, their views, concerns, and experiences of the technology can have impact on their willingness to utilise the tool and consequently the extent to which the tool is integrated in the learning process. A large proportion of research into tertiary students' perceptions in this area focuses on AI in general and chatbots which are not necessarily powered by GenAI, while students' views and experiences of GenAI tools specifically remain relatively underexplored. Research into student perceptions of AI/GenAI typically investigates students' attitudes, their experiences of AI, and factors influencing their perceptions such as gender, disciplines, age, and year of study.

Attitudes towards AI and experiences of AI Research into the use of AI in language classrooms shows that students found AI tools such as chatbots and Plot Generator useful for enhancing language acquisition by providing assistance with grammar, guiding them in generating ideas, and helping them communicate in the target language (Bailey et al., 2021; Sumakul et al., 2020). AI KAKU, a GenAI tool based on the GPT-2 language model, was implemented in English language lessons with Japanese students and was perceived to be easy to use and able to assist students to express themselves in English (Gayed et al., 2022); while the use of AI-based chatbots for learning support improved students' learning achievement, self-efficacy, learning attitude, and learning motivation (Essel et al., 2022; Lee et al., 2022). A study of the use of chatbots in business education also reported favourable user feedback with students citing positive learning experience due to chatbots' responsiveness, interactivity, and confidential learning support (Chen et al., 2023). Most students agreed that AI have a profound impact on their disciplines and future careers (e.g., Bisdas et al., 2021; Gong et al., 2019; Sit et al., 2020) and expressed an intention to utilise AI in their learning and future practice (e.g., Bisdas et al., 2021; Lee et al., 2022), and thus viewed integration of AI as an essential part of university curricula (e.g., Abdelwahab et al., 2022; Bisdas et al., 2021; Gong et al., 2019; Yüzbaşioğlu, 2021).

Students who had a good understanding of AI were also found to express a low level of anxiety about AI in Dahmash et al.'s ( 2020) study. However, Jeffrey's ( 2020) study found conflicting beliefs among college students. Students who had a high level of understanding and information about AI and believed that AI could benefit them personally also expressed concerns about the impact of AI on human jobs. In Dahmash et al.'s ( 2020) and Gong et al.'s ( 2019) research, the choice of radiology as a future career was associated with the impact of AI-The number of medical students indicating radiology as their specialty choice increased when the potential impact of AI was not a consideration. Among the concerns and drawbacks regarding the use of AI, as perceived by students, are limited human interaction/element (e.g., Bisdas et al., 2021; Essel et al., 2022), potential data leakage (e.g., Bisdas et al., 2021), absence of emotional connection (Chen et al., 2023), breach of ethics (e.g., Gillissen et al., 2022; Jha et al., 2022), and reduced job opportunities or increased demand in job practices (Ghotbi et al., 2022; Gong et al., 2019; Park et al., 2020).

Frequency of use/Time spent on AI tools Research examining the relationship between frequency of AI use and student perceptions of AI is inconclusive. For example, Yildiz Durak's ( 2023) study of 86 students in a university in Turkey reported no correlation between chatbot usage frequency and visual design self-efficacy, course satisfaction, chatbot usage satisfaction, and learner autonomy. The finding shows that frequency of use alone is not a meaningful factor, while satisfaction with use can impact users' self-efficacy. In contrast, Bailey et al. ( 2021) found that the amount of time spent on chatbot use in a second language writing class was positively associated with students' confidence in using the target language and perception of task value.

Use of Methodology Most of the research into student perceptions of AI/GenAI employs a quantitative survey design (e.g., Bisdas et al., 2021; Dahmash et al., 2020; Gherhes & Obrad, 2018; Yüzbaşioğlu, 2021). Some studies incorporated open-ended survey questions (e.g., Hew et al., 2023; Jeffrey, 2020) and semi-structured interviews (e.g., Gillissen et al., 2022; Mokmin & Ibrahim, 2021; Park et al., 2020) to gather students' free responses and to probe their views on the research topic in addition to their responses to survey questions. For example, Park et al.'s ( 2020) study consisted of two stages: Semi-structured interviews were conducted face-to-face or by telephone in Stage 1, followed by an Internet-based survey in Stage 2. Studies that examined the impact of AI and student perceptions typically adopted an experimental design using a pretest-intervention-posttest approach and the administration of a questionnaire to examine student perceptions (e.g., Essel et al., 2022; Lee et al., 2022). Qualitative research is relatively rare as only the views of a small number of students can be explored with such an approach. For example, Sumakul et al.'s ( 2020) and Terblanche et al.'s ( 2022) studies based on semi-structured interviews involved eight students and 20 students respectively. In contrast, survey is more effective for reaching a large population of respondents from different geographical locations as shown in previous studies such as Bisdas et al. ( 2021), Dahmash et al., ( 2020), and Gong et al. ( 2019).

Although there has been a considerable amount of research into AI in general as shown in the review of current studies in this section, there is currently lack of investigation into how students perceive GenAI. In view of the unprecedented interest in GenAI at present, there is a need to examine university students' attitude towards GenAI and their experience of using GenAI in order to gain insights into how it can be integrated in higher education to enhance teaching and learning.

Methodology

In this study, we used a survey design to collect data from university students in Hong Kong, exploring their use and perceptions of GenAI in teaching and learning. The survey was administered via an online questionnaire, consisting of both closed-ended and open-ended questions in order a large population of responses. The initial questionnaire was developed by drawing upon similar studies and existing questionnaires on teachers' and students' perceptions of educational technologies in higher education. To ensure the relevance and clarity of the questionnaire items, pilot studies were conducted prior to formal data collection. And the questionnaire was modified based on the feedback from the pilot study. The final version of the instrument comprises a pool of 26 items, employing a 5-point Likert scale ranging from "Strongly agree" to "Strongly disagree," as well as 3 open-ended questions to gather additional insights and perspectives from the respondents. Topics covered in the survey encompassed their knowledge of GenAI technologies like ChatGPT, the incorporation of AI technologies in higher education, potential challenges related to AI technologies, and the influence of AI on teaching and learning.

Data were gathered through an online survey, targeting students from all post-secondary educational institutions to ensure that the results represented the needs and values of all participants. A convenience sampling method was employed to select respondents based on their availability and willingness to partake in the study. Participants were recruited through an online platform and given an informed consent form before completing the survey. The participation was completely voluntary, and the responses were anonymous.

A total of 399 undergraduate and postgraduate students, from various disciplines of six universities in Hong Kong, completed the survey. Descriptive analysis was utilized to analyze the survey data, and a thematic analysis approach was applied to examine the responses from the open-ended questions in the survey. As the total number of responses was manageable (n = 387), two coders manually generated codes. After reading the entire dataset, each coder was assigned the same subset of 50 responses to identify potential themes. In cases where the coders disagreed, they discussed the discrepancies and reached an agreement. Finally, a codebook was created based on consensus and utilized to code the remaining responses.

Results

Demographic information

Participants in this study were from ten faculties (Faculty of Architecture, Arts, Business, Dentistry, Education, Engineering, Law, Medicine, Science and Social Sciences) of six universities in Hong Kong, comprising 204 males (51.1%) and 195 females (48.9%). There were (44.4%, n = 177) undergraduate students and (55.6%, n = 222) postgraduate students. Nearly half of them (55.4%, n = 221) were enrolled in STEM fields, mainly from the Faculty of Engineering (33.1%) and the Faculty of Science (14.5%), while non-STEM students were primarily majored in Arts (14.8%, n = 59), Business (13.3%, n = 53) and Education(7.5%, n = 30). Additionally, 66.7% participants have reported using GenAI technologies in the general context (not specifically for teaching and learning) at least once. Specifically, 21.8% reported rarely using it, 29.1% using it sometimes, 9.8% often using it, and 6.0% reported always using it. Table 1 shows the demographics information.

Knowledge of generative AI technologies

As illustrated in Table 2, participants had a generally good understanding of GenAI technologies, with mean scores ranging from 3.89 to 4.15. Specifically, students had the highest mean score for the statement "I understand generative AI technologies like ChatGPT have limitations in their ability to handle complex tasks" (Mean = 4.15, SD = 0.82) and the lowest mean score for the emotional intelligence and empathy considerations(Mean = 3.89, SD = 0.97), indicating that while they generally understand GenAI technologies has limitations, they may not be fully aware of the potential risks arise from the lack of emotional intelligence and empathy.

Moreover, the data showed a moderate positive correlation between their knowledge of GenAI technologies and frequency of use(r = 0.1, p < 0.05). Specifically, regarding their agreement on if GenAI technologies like ChatGPT may generate factually inaccurate output, students who never or rarely use GenAI technologies (Mean = 3.99, SD = 0.847) were significantly different (t = 2.695, p < 0.01) from students who have used them at least sometimes (Mean = 4.22 SD = 0.829).

Willingness to use generative AI technologies

Overall, the findings suggest that students have a positive attitude toward GenAI technologies. They would like to integrate GenAI technologies like ChatGPT in their learning practices (Mean = 3.85, SD = 1.02), as well as future careers (Mean = 4.05; SD = 0.96). Specifically, students highly value its perceived usefulness in providing unique insights (Mean = 3.74; SD = 1.08) and personalized feedback (Mean = 3.61; SD = 1.06). Additionally, they find these technologies are user-friendly, as they are available 24/7(Mean = 4.12; SD = 0.83) and offer anonymous support services (Mean = 3.77; SD = 0.99).

Moreover, the correlation analysis results show that students' perceived willingness to use GenAI technologies is positively correlated with both knowledge of GenAI (r = 0.189; p < 0.001) and frequency of use (r = 0.326; p < 0.001), indicating that students who are more knowledgeable about these technologies and use them more frequently are more likely to use them in the future (Tables 3, 4).

Concerns about generative AI technologies

Unlike willingness, descriptive statistics show that students expressed a slight favor of concerns about GenAI. They expressed the least positive opinions about if people will become over-reliant on GenAI technologies (Mean = 2.89; SD = 1.13), and the highest rating was for how these technologies could affect the value of university education (Mean = 3.18; SD = 1.16).

Interestingly, there were significant differences between students who never or rarely used these technologies and other participants (t = 3.873, p < 0.01). However, no significant correlation was found between students' concerns and knowledge about GenAI technologies (r = 0.096; p > 0.05).

The benefits and challenges for students' willingness and concerns

What are the reasons behind students' willingness to utilise generative AI technologies?

Consistent with the findings from the quantitative data, most participants perceived GenAI as a valuable tool with numerous benefits and were willing to work with it, primarily on learning, writing and research purposes:

1.
Personalized and immediate learning support

When students struggle with assignments, GenAI can act as a virtual tutor, providing personalized learning support and answering their questions immediately. A student from the faculty of engineering considered AI as " a top student" in their class, because " When I have doubt and couldn't find other people to help me out, ChatGPT seems like a good option." Besides immediate answers, customized recommendations and feedback were also valued by students. As one remarked, " It would be useful if ChatGPT could help me find the most effective solution when I am checking my finished homework. This way of using it would help me improve my depth of thinking and understanding. " Feedback on submitted assignments is essential for students' learning, but it also puts a lot of pressure on teachers, especially with a large number of students. In this case, GenAI may be a solution.

Moreover, AI can also provide learning resources tailored to students' specific needs. For example, a student majoring in English proposed an innovative learning methods, using ChatGPT to learn a second language, " ChatGPT can generate short texts based on the words entered by the user to help students memorize the words." Moreover, some students from the Faculty of Education also assumed that AI can assist them in future teaching, e.g., " I believe that the use of ChatGPT will reduce teachers' workload for answering questions. I may also use it to generate some lesson plans." Since GenAI was considered to " improve students' motivation" and "help students learn better on their own", in the future, it may potentially revolutionize traditional teaching and learning methods.

2.
Writing and brainstorming support

GenAI technologies, such as ChatGPT, can also be used as writing assistants. Sometimes, students find it difficult to generate ideas or find inspiration. In such cases, a participant suggested, " It'll be convenient to ask ChatGPT some general questions and even get inspired by it." By inputting a question related to the writing topic, the AI output can serve as a starting point to develop and expand on their ideas. In addition, this virtual assistant is equipped to provide technical support, for example, " it can help with formatting and information retrieval "or " help gather citations. ", which improves efficiency.

Furthermore, after writing, students can also use GenAI to enhance their writing skills. As one remarked, " I would use it to help improve my writing (grammar, paraphrasing...), consult some questions or let it give some feedback on my writing." Especially for non-native English-speaking students who are struggling with writing, it can be particularly useful if AI can " help polish articles " and provide personalized feedback for their written texts.

3.
Research and analysis support

The role of GenAI technologies in research has also caught the attention of students. In terms of its ability to acquire, compile, and consolidate information, some participants suggested it can " facilitate literature searching," " summarise readings," and " even generate hypotheses based on data analysis." With a vast amount of data and knowledge, AI-powered technologies can help researchers always stay up-to-date with the latest research trends. Moreover, it also contributes to data collection and analysis. A student noted, " It saves resources in data collection and initial analysis. We should ride on the initial insights to build our own insights. " Since GenAI technologies are capable of rapidly and effectively processing large amounts of data, students can directly work on the basis of the preliminary analysis results.

4.
Visual and audio multi-media support

In addition to the above-mentioned uses, participants also used GenAI technologies for creating artworks and handling repetitive tasks. With advances in computer vision, AI-generated artworks have particularly gained attention from STEM students. A student from the faculty of science mentioned, " I mainly played around with DALL-E, stable diffusion and other AI art technologies, which generate images based on a prompt." Similarly, an engineering student " used text-to-image generation AI like stable diffusion at home to create artwork." Furthermore, AI technologies can facilitate "the production of multi-media", incluing slides, audios, and videos. As a content creator, " when we have no clue how to visualize stuff, it can offer samples and insights. "

What are the reasons behind students' concerns or lack of concerns regarding generative AI technologies?

In alignment with the quantitative results, the qualitative data similarly revealed different concerns regarding challenges about GenAI. Some participants were optimistic about AI's integration in the future. The reasons for this optimism include the willingness mentioned earlier, as well as the belief that GenAI is part of the evolution and trends of technology. One student stated, " It follows a general revolution of technology, similar to the public use of computers 40 years ago. I'm very much looking forward to the future of how such technology can reshape the world" They suggested that as new technologies emerge, it is better to " positively embrace it " rather than avoid them.

Another reason behind the optimism was the assumption that humans would still maintain control and oversight over the GenAI. A participant remarked that " I am not that concerned, as it would lead humans to smartly utilize such AI tools to complete their tasks in an efficient manner rather than simply being replaced by such tools." Another postgraduate student from the Faculty of Arts emphasized that AI is not a replacement for human skills and expertise: " to my best of knowledge, I feel ChatGPT has not yet had the creativity and imagination as human beings, nor can it create a thesis for postgraduate students. " At least for now, they believed humans would continue to be in the loop and have oversight over the GenAI technologies.

However, more than half of the participants still have concerns about the challenges of integrating GenAI technologies, mainly about the reliability of the technology itself and its impact:

2.
Challenges concerning privacy and ethical issues

The use of GenAI also raised privacy and ethical concerns, which was mostly mentioned by students majored in arts and social science. They were worried that AI would collect personal information from our messages. As a social science student put forward, " AI technologies are too strong so that they can obtain our private information easily." Since these messages will be used to further improve the system, if they are not properly protected, it " can pose privacy and security risks. "

Ethically, the plagiarism concern has been mentioned numerous times. Plagiarism has long been a critical issue in academics. But, with the rapid development of GenAI technologies, it has become increasingly difficult to identify plagiarized information. As an art student remarked, " I want to know whether I am dealing with an AI bot or AI-generated content. Right now, it is somewhat easy to detect, but as the technology improves, it may not be so easy ".

4.
Challenges concerning career prospects

Regarding its impact on society as a whole, GenAI also carries risks and drawbacks. The most frequently mentioned concern is job replacement. As GenAI is transforming the workplace, some jobs that students are preparing for may disappear. A computer science student expressed his concern " I will probably lose my job in the future due to the advent of ChatGPT." Similarly, a student who majored in social science also mentioned, " AI may replace the job that I'm interested in (e.g., GIS analyst)". Consequently, employers may also raise their recruitment requirements. This development will pose a test for future graduates, since " those who fall behind on this might have difficulty finding employment or catching up. "

5.
Challenges concerning human values

Another mentioned societal risk relates to the value system. Some participants were worried that "AI could misalign with our human values and becomes a danger to us." For example, it may contribute to social injustice and inequality, as some participants noted, " it may widen the gap between the rich and the poor" and " also be unfair to those students who don't use it." Furthermore, in academic institutions and education, some were concerned that the widespread use of AI might also might affect the student-teacher relationship, since students may be "disappointed and lose respect for teachers."

Discussion

The study of student perceptions of GenAI, such as ChatGPT, in higher education reveals a complex and nuanced picture of both enthusiasm and concerns. The findings of this study provide an insightful understanding of university students' perception. It is evident that students are generally familiar with GenAI technologies, and their level of familiarity is influenced by factors such as knowledge about GenAI and frequency of use. The results also highlight the potential benefits and risks associated with using GenAI in teaching and learning, which are perceived differently among students based on their experiences with GenAI technologies. Overall, the participants showed a good understanding of the capabilities and limitations of GenAI technologies, as well as a positive attitude towards using these technologies in their learning, research, and future careers. However, there were also concerns about the reliability, privacy, ethical issues, and uncertain policies associated with GenAI, as well as its potential impact on personal development, career prospects, and societal values. Table 5 shows the benefits and concerns of employing GenAI technologies.

The study revealed that students' knowledge of GenAI technologies and frequency of use are positively correlated. This suggests that exposure to these technologies and hands-on experience may help in enhancing students' understanding and acceptance of GenAI. Also, despite the relative novelty of GenAI for public use, students appear to have knowledge of the technologies and understand its benefits and risks quite well. Both quantitative and qualitative findings also show that students are generally willing to use GenAI for their studies and future work, but they have high expectations. For example, the study found that students perceive GenAI technologies as beneficial for providing personalized learning support as they expect learning resources tailored to their needs 24/7. In terms of writing and brainstorming support, students want feedback to improve writing skills, beyond just grammar checking and brainstorming, similar to the findings in Atalas' study ( 2023). For research and analysis support, students envision GenAI capabilities to not only facilitate literature searching and summarizing readings but also to generate hypotheses based on data analysis, enabling them to stay up-to-date with the latest research trends and build upon initial insights for their own work (Berg, 2023) which would not be expected from previous educational technologies. These findings indicate the potential of GenAI in revolutionizing traditional teaching and learning methods by offering tailored assistance, diverse learning needs, promoting efficiency and fostering self-directed learning.

Despite the positive outlook, the study also reveals challenges concerning GenAI technologies, with students expressing reservations about over-reliance on the technology, its potential impact on the value of university education, and issues related to accuracy, transparency, privacy, and ethics. Students express concerns about the accuracy and ethical issues, particularly plagiarism, as they face difficulty in determining the originality of work generated by GenAI tools (Peres et al., 2023), which are unable to assess validity or identify falsehoods, thus necessitating human oversight (Lubowitz, 2023). Interestingly, there is no significant correlation between students' concerns and their knowledge about GenAI technologies, suggesting that even those with a good understanding of the technology may still have reservations, similar to Dahmash et al. ( 2020)'s findings. Additionally, students were apprehensive about GenAI, which may hinder critical thinking and creativity, and the impact of GenAI on job prospects (Ghotbi et al., 2022; Gong et al., 2019; Park et al., 2020) and human values (Gillissen et al., 2022; Jha et al., 2022).

User acceptance is key to the successful uptake of technological innovations, and students are the primary users of educational technologies. By understanding how students perceive generative AI technologies, educators and policymakers can better understand how best to integrate these technologies into higher education to enhance teaching and learning outcomes.

As mentioned, the reasons behind students' willingness and concerns about GenAI technologies are multifaceted. On one hand, students are optimistic about the future integration of these technologies into their academic and professional lives, considering GenAI as part of the ongoing technological evolution. On the other hand, students have reservations.

Conclusion

In this study, student perception of GenAI technologies were investigated. According to Biggs ( 1999, 2011), student perceptions of their learning environment, abilities, and teaching strategies significantly influence their learning approach and outcomes, with positive perceptions leading to a deep learning approach and negative perceptions resulting in a surface approach. Thus, it is vital to understand student perception in the context of GenAI technologies. By taking students' perceptions into account, educators and policymakers can better tailor GenAI technologies to address students' needs and concerns while promoting effective learning outcomes.

Understanding students on their willingness and concerns regarding the use of GenAI tools can help educators to better integrate these technologies into the learning process, ensuring they complement and enhance traditional teaching methods. This integration can lead to improved learning outcomes, as students will be more likely to adopt a deep approach to learning when they perceive GenAI as a valuable and supportive resource. Students' perceptions can provide insights into their level of AI literacy, which is essential for responsible use of GenAI technologies. By identifying gaps in students' understanding, educators can develop targeted interventions to improve AI literacy and prepare students for future employment in an increasingly AI-driven world. In the findings, students highlight the potential risks and concerns, educators can create guidelines and safeguards that ensure responsible and ethical use of GenAI technologies.

Implications

The diverse range of opinions among the participants highlights some implications that must be considered to ensure the successful integration of GenAI into higher education. According to the 3P model proposed by Biggs ( 2011), three key elements that can influence learning outcomes include student-dependent factors, teaching-dependent factors, and interactive impacts from the whole system. With this framework in mind, it is important for students to develop their AI literacy, which includes understanding the basics of Generative AI, how it works, its advantages, and disadvantages, as well as different uses in higher education. Meanwhile, when using Generative AI, they should ensure that their use aligns with ethical principles and does not cause any harm to society.

Additionally, as some students expressed concerns about their holistic competencies' development, teachers can play a vital role in developing their high-order skills, perhaps with the help of GenAI as mentioned in (Chan & Tsi, 2023). For example, teachers can encourage students to critically evaluate AI-generated content and distinguish between reliable and unreliable sources to develop their critical thinking skills. Or Generative AI can be used to spark students' creativity by generating diverse and unpredictable ideas and prompts. Since holistic competencies may become the most in-demand attributes for today's work environment, a focus on competency development in instructional designs could also relieve students' anxiety concerning career prospects.

In the foreseeable future, as generative AI may potentially be widely used in formal academic settings, institutions should also develop policies and provide formal guidance on the use of Generative AI. Chan ( 2023b) suggests an AI Ecological Education Policy Framework to tackle the various implications of AI integration in university teaching and learning with three dimensions: Pedagogical, Governance, and Operational. Firstly, institutions should consider providing educational resources and workshops to familiarize students with GenAI technologies and their ethical and societal implications. This would enable students to make informed decisions when using these technologies in their academic endeavors.

Secondly, the development and implementation of GenAI technologies should prioritize transparency, accuracy, and privacy to foster trust and mitigate potential risks. For example, technical staff could work on explainable AI models that provide clear explanations of their decision-making processes. In addition, robust data protection policies and practices should be in place to safeguard users' privacy.

Lastly, higher education institutions should consider rethinking their policy, curricula and teaching approaches to better prepare students for a future where GenAI technologies are prevalent. This may involve fostering interdisciplinary learning, emphasizing critical thinking and creativity, and cultivating digital literacy and AI ethics education.

In conclusion, this study sheds light on the diverse perspectives of university students towards GenAI technologies and underscores the need for a balanced approach to integrating these technologies into higher education. By addressing students' concerns and maximizing the potential benefits, higher education institutions can harness the power of GenAI to enhance teaching and learning outcomes while also preparing students for the future workforce in the AI-era.

Limitations and future research

This study has several limitations that should be considered when interpreting the findings. First, the sample size was relatively small, which may limit the generalizability of the results to the broader population of students in Hong Kong. The study's reliance on self-reported data may also introduce potential biases, as participants could have been influenced by social desirability or inaccurate recall of their experiences with GenAI technologies. Furthermore, the cross-sectional design of the study does not allow for an examination of changes in students' perceptions over time as their exposure to and experiences with GenAI technologies evolve. Lastly, since Gen AI has not been fully used in formal academic settings, students have limited exposure to it. This study did not explore how students were exposed to AI and the actual impact of GenAI on students' learning outcomes, which would be necessary to provide a more comprehensive understanding of the role of these technologies in education.

Future research should address these limitations by employing larger, more diverse samples; using longitudinal designs to track changes in students' perceptions of generative AI over time and explore how these technologies are integrated into higher education; and examining the relationship between GenAI use and learning outcomes. Additionally, future research could explore on a specific group of students from different discipline, academic backgrounds, age groups, or cultural contexts on AI literacy.

Overall, there is a need for further research to better understand how best to integrate generative AI into higher education while minimizing potential risks related to privacy and security. By exploring these areas, we can ensure that these technologies are used responsibly and effectively in teaching and learning contexts.

Availability of data and materials

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

References

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Acknowledgements

The author wishes to thank the students who participated the survey.

Funding

No funding has been received for this study.

Author information

Authors and Affiliations

University of Hong Kong, Hong Kong, China

Cecilia Ka Yuk Chan & Wenjie Hu

Contributions

CKYC: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing-original, Writing-review & editing, Supervision, Project administration. WH: Methodology, Formal analysis, Investigation, Writing-original, Visualization. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Cecilia Ka Yuk Chan.

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Chan, C.K.Y., Hu, W. Students' voices on generative AI: perceptions, benefits, and challenges in higher education. Int J Educ Technol High Educ 20, 43 (2023). https://doi.org/10.1186/s41239-023-00411-8
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Keywords

ChatGPT

Generative AI

Student perception

AI literacy

Risks

Advantages

Holistic competencies

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Students' voices on generative AI: perceptions, benefits, and challenges in higher education - International Journal of Educational Technology in Higher Education

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This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning.

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AI is complicating plagiarism. How should scientists respond?

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The explosive uptake of generative artificial intelligence in writing is raising difficult questions about when use of the technology should be allowed.

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Eamon: Generative AI and College Students: Use and Perceptions.

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Eamon: Generative AI and College Students: Use and Perceptions.

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I found this site through the sculpture library databases.

Link:https://research-ebsco-com.libproxy.scu.edu/c/z7wra6/search/details/m73tqpu2ir?db=a9h&limiters=None&q=college%20students%20generative%20ai

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I used google's search engine to find this article

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Over half of students BestColleges surveyed (54%) say that the use of AI tools on college coursework counts as cheating or plagiarism.

53% of students say they have had coursework that requires them to use AI as part of an assignment.

Nearly 80% of students say they have had at least one instructor discuss the use and ethics of AI in the classroom.

More than half of students (58%) say that their school or program has a policy about the use of generative AI tools to complete assignments or exams.

More than one-quarter (28%) say some courses/professors have a policy and some do not.

Last year, OpenAI released its large language model called ChatGPT, a chatbot capable of generating answers almost instantaneously - whether that be writing essays, summarizing books, or explaining difficult math problems. Students suddenly had access to a new, powerful technology.

Now a year later, BestColleges has surveyed 1,000 current undergraduate and graduate students to hear their perspectives on AI in higher education. Results show that 56% of college students have used AI on assignments or exams. Another 41% of students say they have not, and 4% prefer not to answer.

At the same time, professors are assigning students specifically to use these tools. Just over half of students (53%) say they have had coursework that requires them to use AI as part of an assignment. And, nearly 8 in 10 students (79%) say they have had an instructor talk to them about the use and ethics of AI in the classroom.

The majority of students (54%) say using AI on schoolwork or exams counts as cheating or plagiarism. Around 1 in 5 students (21%) say that using AI to complete assignments or exams is not cheating or plagiarism. One-quarter of students remain neutral.

In our March survey, a much smaller percentage of students (22%) responded that they had used AI tools to help complete assignments or exams. A similar percentage of students (51%) as the new survey, however, had still believed using AI on exams constituted cheating.

In an October webinar called "Applying AI to Improve Courses, Teaching, and Learning," moderator Andrew Maynard, a professor at Arizona State University, emphasized that using AI to cheat is a short-term problem. He argued the education system must fundamentally change to create better assessments and better prepare students with AI skills for a changing job market.

According to Hadi Partovi, CEO of the nonprofit Code.org, AI is the most important topic for students to learn. Possibly even more than math.

How Are Colleges Responding to the Use of AI?

More than half of students (58%) say that their school or program has a policy about the use of generative AI tools to complete assignments or exams. One in 10 students (10%) say their school or program does not have an AI policy, and 5% do not know.

More than one-quarter (28%) say it differs -some courses or professors communicate a policy, and some do not.

Students whose programs do have an AI usage policy say this policy was communicated to them through their course syllabus (65%), email (43%), or a student handbook or honor code (42%).

Since the release of ChatGPT, companies have alternatively released AI detection tools. For example, the popular plagiarism tracker Turnitin launched an AI detection tool for college professors, which pairs with their other programs that check for originality. Turns out, these tools are pretty accurate.

Discover More Data About AI Use in College

Six in 10 college students say the use of AI tools in college diminishes the value of earning a degree. Plus, 40% percent worry about the influence of AI on their major.

AI Tools in College Most Popular Among Business and STEM Majors, Men, and Millennials

Some student groups are more likely to report using AI tools to complete college coursework than others. Business and STEM majors, men, and millennials are more likely than humanities majors, women, and Gen Z to report using the tools.

AI Tools More Popular Among Business Students

More business majors report having used AI tools such as ChatGPT to help complete assignments or exams than humanities majors (62% vs. 52%). STEM majors are somewhere in between, with 59% responding that they have used AI tools to complete assignments or exams.

Business majors are also most likely to respond that they had coursework that required them to use AI as part of an assignment - over 61% of business majors compared to 45% of humanities majors and 58% of STEM majors.

In addition to being most likely to use AI tools, business majors are least likely to say that using AI tools to complete assignments or exams is cheating or plagiarism. Just 51% of business majors consider using AI tools to complete assignments or exams as cheating or plagiarism, compared to 57% of humanities majors and 55% of STEM majors.

Men Far More Likely to Report Using AI Tools Than Women

A higher percentage of men than women report having used AI tools such as ChatGPT to help complete assignments or exams (64% vs. 48%).

Men are also more likely than women to answer that they have had coursework that required them to use AI as part of an assignment (62% vs. 44%).

It is worth noting that a much higher percentage of men make up business and STEM majors, which were the fields that required students to use AI tools at a higher rate than others. In our survey, 1 in 5 men (20%) were business majors compared to 16% of women. Only 3% of women were in computer and information sciences and support services compared to 13% of men.

A similar percentage of men and women (56% vs. 52%) agree that using AI tools to complete assignments or exams is cheating or plagiarism.

6 in 10 Millennial Students Have Used AI Tools vs. Half of Gen Z

More millennial students report having used AI tools such as ChatGPT to help complete assignments or exams than Gen Z students (62% vs. 52%).

A much higher percentage of millennial students than Gen Z say that they have had coursework that required them to use AI as part of an assignment (65% vs. 46%).

Despite using AI tools at a higher rate, millennials are also more likely than Gen Z students to say that using AI to complete assignments or exams constitutes cheating or plagiarism (56% vs. 53%).

Methodology

This survey was conducted from Sept. 29-Oct. 5, 2023, and was fielded by Pure Spectrum. Survey participants included 1,000 respondents nationwide who were currently enrolled in an on-campus, online, or hybrid undergraduate or graduate degree program. Respondents were 17-49 years of age, with the majority (95%) ages 18-38, and currently pursuing an associate, bachelor's, master's, doctoral, or professional degree. The respondents for the survey were screened by various quality checks, including systems like Relevant ID, and responses were manually reviewed to ensure consistency and accuracy.

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56% of College Students Have Used AI on Assignments or Exams | BestColleges

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Over half of students BestColleges surveyed (54%) say that the use of AI tools on college coursework counts as cheating or plagiarism. Find more survey data in our report.

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Breaking news and analysis from TIME.com. Politics, world news, photos, video, tech reviews, health, science and entertainment news.

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Found on The New York Times

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What Students Are Saying About Teachers Using A.I. to Grade

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Teenagers and educators weigh in on a recent question from The Ethicist.

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Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education  

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Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education  

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I found this through searching the SCU data base on the home page - Samantha Weber

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Title:

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education

Collection:

Computer Science
Physics (Other)

Subject Terms:

Physics - Physics Education, Computer Science - Artificial Intelligence

More Details:

Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

Document Type:

Working Paper

Access URL:
http://arxiv.org/abs/2412.02653

Accession Number:

edsarx.2412.02653

# data[].attributes.attachment_link.description
Title: Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education Collection: Computer SciencePhysics (Other) Subject Terms: Physics - Physics Education, Computer Science - Artificial Intelligence More Details: Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge.

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I found this on google

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Generative AI (GAI) has quickly gained a significant foothold in academia, and is now used widely for teaching, learning, and research purposes. While national trends in student and faculty adoption are unclear, surveys conducted by individual institutions have found that approximately 50 percent to 65 percent of both students and faculty have used ChatGPT or one of its commercial competitors. If current trends continue, in the near future GAI use will be ubiquitous, fully integrated into the core mission of colleges and universities.

Understanding the value of individual products in a now-crowded marketplace is a major challenge for end users and for university CIOs, IT departments, and others involved in decision-making.

While the full effects of this transformation are, at best, clear only in outline, 2023 saw both well-established vendors and start-ups racing to bring GAI applications optimized for use in educational contexts to market. As we write this brief, new products are appearing so rapidly that just keeping up with them is difficult, and understanding the value of individual products in a now-crowded marketplace is a major challenge for end users and for university CIOs, IT departments, and others involved in decision making about which products will be supported and/or licensed for campus users.

Since last fall, Ithaka S+R has been partnering with 19 colleges and universities from the US and Canada to assess GAI's impact on higher education and make evidence-based, proactive decisions about how to manage the far-ranging effects of GAI. As part of this project, Ithaka S+R has been cataloging GAI applications geared towards teaching, learning, and research in the higher education context. Today, we are excited to make our Product Tracking tool ( https://sr.ithaka.org/our-work/generative-ai-product-tracker/) publicly available.

The Product Tracker includes a basic description of each tool, as well as information about the pricing model, key features, and other relevant details such as the large language model (LLM) or datasets behind the tool or background on the vendor. As it would be impossible to track every GAI application that might conceivably be used in higher education contexts, we have limited the Tracker to a) products marketed specifically towards faculty or student use, and b) products that appear to be in active use in teaching, learning, or research activities. At present, the Product Tracker contains data on over 100 GAI tools and applications. While we make no claims to including every relevant or potentially relevant product, the Tracker includes data on the most visible individual products on the market and is comprehensive enough to provide a landscape perspective on the market itself. The Tracker is also a living document which we update regularly to include new products or add new data about existing products. We will continue to do so for at least the remainder of 2024 if the tool retains value to the community.

This issue brief is designed to enrich the descriptive data captured in the Product Tracker. In the brief's first section, we provide a typology of existing products and value propositions. In the second, we offer observations about what the product landscape suggests about the future of teaching, learning, and research practices, and speculations on the near-term future of the academic GAI market.

Use Cases and Value Proposition

At the most fundamental level, most products included in our tracker are marketed as ways of speeding up and/or enriching three key phases of the research lifecycle: discovery, understanding, and creation. Some products are designed to perform hyper-specific tasks such as providing analysis of a single PDF; others are designed to support students and researchers from the conception of a project to its completion.

In nearly every case, it is important to recognize that individual products are essentially apps running on the "operating system" of a commercial LLM, and specifically on OpenAI's GPT models. With a few notable exceptions, primarily coming out of biomedical fields, ground-up, custom-built LLMs are not part of the current product landscape.

Discovery

GAI powered discovery tools are at this point perhaps the most mature application of the technology to higher education contexts, driven in part by the integration of GAI into existing search and discovery tools used by major scholarly publishers and aggregators. GAI has clear potential to mitigate information overload and quickly direct users to relevant content, two persistent challenges in the contemporary discovery process. GAI's ability to engage users in dialogue and its use of natural language processing over keywords and metadata intend to simplify and improve the specificity of the discovery process. Users benefit from these tools' capacity to gain a deep semantic understanding of both natural language queries and the content of academic papers in databases to have an increasingly conversational search experience. For instance, Consensus ( https://consensus.app/), a startup with an AI-powered search engine, now has a Copilot feature in its main product as well as a Consensus GPT in OpenAI's store, both of which bring a chat dimension to discovery.

Moreover, the discovery products aimed at higher ed users address a clear and significant problem with using consumer LLMs such as ChatGPT for research and educational purposes, namely the poor quality and incorrect data included in their training data and their limited ability to cite specific sources. The potential of GAI powered tools to simultaneously address the limitations of both existing academic discovery tools and of general purpose LLMs provide a compelling value proposition that improves an identifiable use case applicable to undergraduate student assignments and specialist research questions.

Major scholarly publishers and content aggregators have the advantage of being gatekeepers to large corpora of such material and have spent the past year racing to integrate GAI into their discovery platforms.

High quality discovery depends on high-quality content, and one of the core ways vendors are differentiating their offerings is based on the value of the datasets that their tools provide access to. Major scholarly publishers and content aggregators have the advantage of being gatekeepers to large corpora of such material and have spent the past year racing to integrate GAI into their discovery platforms. The growth of open access publishing has created opportunities for start-ups to enter this discovery space as well. Consensus, for example, pulls its source material from the over 200 million scientific papers in the open Semantic Scholar database ( https://www.semanticscholar.org/), with particular strengths in queries related to "medical research and physics to social sciences and economics."

Understanding

With partial exceptions such as ResearchRabbit ( https://www.researchrabbit.ai/), "the Spotify for papers," and Keenious ( https://keenious.com/), few vendors are investing in products designed primarily to perform traditional search better. GAI's ability to help users understand relevant material through summarization and synthesis is the core value proposition of many products on the market.

This is invariably the case with the tools released by large publishers and aggregators. For example, ITHAKA's JSTOR GAI assistant ( https://www.jstor.org/generative-ai-faq), currently in beta release, allows users to ask questions about, generate summaries of, and find other content similar to the content they are viewing. Elsevier's Scopus AI ( https://www.elsevier.com/products/scopus/scopus-ai) search can provide summaries from abstracts of documents relevant to the user's search within the Scopus database, suggest "go deeper" questions, and identify other top researchers in the same field. Digital Science's Dimensions AI Assistant ( https://www.dimensions.ai/products/all-products/dimensions-ai-assistant/), in beta, generates summaries from the Dimensions dataset based on user's queries. Commercial start-ups like Consensus, and not-for-profit vendors like Ought's Elicit ( https://elicit.com/welcome) or the Allen Institute for AI's Semantic Scholar ( https://www.semanticscholar.org/), are building products with similar functionality that use millions of open access research publications. Scite ( https://scite.ai/), a startup purchased by Research Solutions in November 2023, draws from open repositories such as PubMed and Unpaywall, but is also signing indexing agreements with commercial publishers.

These tools point towards a future in which the distinction between the initial act of identifying and accessing relevant sources and the subsequent work of reading and digesting those sources is irretrievably blurred if not rendered irrelevant. For organizations providing access to paywalled content, it seems likely that many of these new tools will soon become baseline features of their user interface and presage an era where that content is less "discovered" than queried and in which secondary sources are consumed largely through tertiary summaries.

Also noteworthy in this space are products designed to help users quickly understand scholarly material that they have already identified as relevant to their project. Scholarcy ( https://www.scholarcy.com/), ChatPDF ( https://www.chatpdf.com/), Adobe's AI Assistant ( https://www.adobe.com/acrobat/generative-ai-pdf.html), and the aptly named TLDR This ( https://www.tldrthis.com/) and Explainpaper ( https://www.explainpaper.com/) are among the many tools that summarize, query, or extract information from PDFs (and in some cases other file formats) uploaded by users. The fundamental value proposition for many offerings in this space is their promise of allowing quicker and better comprehension of lengthy and complex material.

The most comprehensive products in this category combine various different features into a workflow platform. Such products present themselves as one-stop shops for the different steps of the research process. SciSpace ( https://typeset.io/), for instance, combines the capability to query uploaded documents with a search engine that can also help identify related papers. It also contributes to the writing process by paraphrasing the user's text in different styles, and checks texts for AI presence. Products such as genei ( https://www.genei.io/), Notion ( https://www.notion.so/product), and Iris.ai ( https://iris.ai/) present themselves as workspaces that help users organize their research data in addition to having GAI features. Genei, for example, offers a search engine and the ability to extract keywords or summaries from search results or user uploaded documents that can be stored with other files on the platform. It additionally includes a citation generator and a tool that rephrases or expands users' writing. As we will discuss further below, these multi-function platforms may have an edge in the market because of their ability to combine the capabilities of multiple tools being marketed separately into one workspace.

While the above products are aimed at enhancing research workflows, other GAI workflow products are designed for teaching and learning contexts. An example is Kortext Premium ( https://www.kortext.com/premium-live/), an enhanced version of the Kortext study platform. This multipurpose workspace provides students access to digital textbooks and file storage, as well as generating study notes, summaries, translations, and citations. Another student learning-oriented product is Clarivate's Alethea ( https://clarivate.com/products/books/alethea/), a reading assistant that asks students questions and creates tasks for them to facilitate engagement with their reading. Alethea thus advertises itself as using GAI to make students think more critically, rather than producing work for them. In essence, products geared towards students aim to facilitate students' processes of gathering important information from course materials, as well as verifying and reinforcing comprehension.

Creation

Content generation is central to the very definition of GAI, and while content generation is a part of nearly all GAI products, we include in this category products whose primary function for teachers, researchers, or students is generating text, images, or code.

Text-generating tools market themselves as simplifying the process of moving from unformed ideas to polished text. Some products in this space use GAI to improve functions that have been part of word processing software for decades. For example, the popular platform Grammarly's ( https://www.grammarly.com/) GAI features utilize user inputs to generate lists of ideas during brainstorming, as well as produce written drafts. Products such as Jenni ( https://jenni.ai/) and Quillbot ( https://quillbot.com/) auto-complete users' sentences. Citation generators and plagiarism detectors are other examples of GAI products serving well-established higher ed use cases.

Perhaps the most exciting developments in this space are several products designed specifically to facilitate scientific communication. Prominent products in this space include Springer-Nature's Curie ( https://www.aje.com/curie/), a Microsoft Word Plugin that provides suggestions about how to improve the flow and structure of academic writing, Digital Science's Writefull ( https://www.writefull.com/), and Trinka ( https://www.trinka.ai/). All three products are designed to better understand the terminology and structure of academic writing than general purpose writing tools, thanks to their specialized training on scientific publications. Several tools in this category include features designed to assess what Trinka calls the "publication readiness" of manuscripts. Trinka includes tools to cross-check a manuscript against the scope of a particular journal, generate abstracts and keywords, and verify compliance with individual journals' technical and ethical standards. HeyScience's Intelligent Review tool ( https://reviewer.heyscience.ai/) generates feedback on manuscripts tailored to the journal that a researcher has identified as a potential venue for publication. Such tools have the potential to level the playing field for academics publishing in a non-native language, as well as to simplify the process of making a manuscript more likely to be accepted for publication.

Beyond text generation, GAI is known for its capacity to generate images, popularized by tools such as OpenAI's DALL-E ( https://openai.com/dall-e-3), Midjourney ( https://www.midjourney.com/home), Stability AI's Stable Diffusion ( https://stability.ai/stable-diffusion), and Adobe Firefly ( https://www.adobe.com/sensei/generative-ai/firefly.html). The baseline function of this family of tools is to allow users to create and manipulate images based on text inputs. Certain products have additional offerings: Stability AI, for example, has also released GAI-powered tools that create music and sound effects and video. While these tools that generate images and other media are not marketed exclusively for teachers, researchers, or students, they are often included on lists of products that may find use in educational contexts.

Researchers and students are also finding products that generate code useful. Meta's Code Llama ( https://ai.meta.com/blog/code-llama-large-language-model-coding/), for example, produces code and natural language about code from both code and natural language inputs. Github Copilot ( https://github.com/features/copilot), along similar lines, also allows users to chat about their code base and receive code suggestions as outputs. It also includes a "code completion" feature. Startup coding products with similar capabilities include Source AI ( https://sourceai.dev/), Replit AI ( https://replit.com/ai), and Tabnine ( https://www.tabnine.com/). Often, such products can help debug code too.

Tools aimed specifically at teachers and students are more common in the creation space than in the discovery or understanding space. For example, Anthology's AI Design Assistant for Blackboard Learn Ultra ( https://www.anthology.com/ai-design-assistant) and the startups Teachermatic ( https://teachermatic.com/) and Curipod ( https://curipod.com/) generate materials teachers can use in the classroom, such as lesson plans, worksheets, tests, or rubrics. These teacher-oriented content generators market themselves as saving teachers valuable time by facilitating the process of creating course materials and sometimes offer options to tailor the content to meet student needs. Other content generators are geared towards learning reinforcement for students. Wolfram's Problem Generator ( https://www.wolframalpha.com/problem-generator/), for instance, creates practice math problems and worksheets. The startup Wisdolia ( https://www.wisdolia.com/) creates study flashcards based on content uploaded by users. Such products offer students customizable ways to practice what they have been learning in the classroom on their own time.

What's Next for Higher Education?

As the sheer size of our Product Tracker indicates, the marketplace for GAI products useful to students and faculty is growing more crowded daily. The maturation of the GPT Store as a venue for reaching customers and the ease of building custom versions of ChatGPT indicate that we will continue to see new entries coming to market for some time. At the same time, many of the products in our Tracker are essentially interchangeable in terms of their core features and functionality. Products that cannot differentiate themselves or their value propositions are likely fated to fold, be acquired by bigger players, or limp along at the margins of the market. Because of the low barrier to entry involved in customizing an instance of ChatGPT, it is reasonable to expect products and players to continue to proliferate and the market to eventually consolidate around a small number of companies.

Products that cannot differentiate themselves or their value propositions are likely fated to fold, be acquired by bigger players, or limp along at the margins of the market.

In academic spaces, the control that large commercial publishers have over large and unique portions of the most important form of scholarly content (particularly in research contexts) is a significant competitive advantage. This content adds considerable value to the discovery tools they are developing and can serve as a foundation for unified platforms with functionality across the discovery, understanding, and writing processes. What is less clear to us is how the significant investments these publishers have made in open publishing models may compete with the increased value of closed collections over the longer term. Already, the sizable corpus of OA publications has given not-for-profit and commercial competitors access to the content necessary to compete in this space: GAI may dramatically increase the commercial value of being a gatekeeper to otherwise inaccessible scholarly content.

Tools embedded within larger platforms are likely to gain advantages reaching higher education users in contexts beyond research and scholarly publishing. Learning management systems could function similarly in teaching and learning contexts. One example is the aforementioned AI course design assistant for Blackboard; another is D2L's beta features that use GAI to create quizzes and practice questions ( https://www.d2l.com/newsroom/d2l-rolls-out-generative-ai-program-practice-quiz-questions/). Given the central role that learning management systems play in college courses, these organizations seem well positioned to build out suites of GAI applications aimed at course instructors and their students. The large office suites offered by Microsoft and Google are other potential vessels for the platformization of GAI in higher education.

Lurking behind both the diversity of the current marketplace and possible future of a smaller pool of major platforms is OpenAI, whose dominance of GAI in higher education-and other major economic sectors-is difficult to overstate. The vast majority of the products mentioned in this issue brief and included on our Product Tracker run on Open AI's GPT models. Other significant LLMs such as Meta's Llama or Google's Gemini are bit players and, if anything, may be losing ground to OpenAI. The slow but steady stream of universities entering into licensing agreements with OpenAI to provide GAI services to campus communities are another key measure of OpenAI's rapidly deepening entanglement with the IT infrastructure of higher education. If OpenAI and Microsoft (thanks to its close relationship to OpenAI) continue to consolidate their current advantage, they will be the true "winners" in the market.

OpenAI's dominance raises important questions for the future of GAI in higher education, particularly as universities inch towards fully incorporating the technology into their teaching, learning, and research missions. One significant unknown is what the market price for OpenAI's services will be once OpenAI shifts from pursuing market share to profit maximization. Universities are likely also unsure how many tokens might be required to fully integrate GAI across campus users and use cases, making it doubly difficult to understand the financial aspects involved in going all in on GAI.

A second important question relates to the opportunities and costs of normalizing GAI use in pedagogical and research contexts. Technology profoundly shapes the cognitive aspects of learning and how institutions articulate and measure it, and OpenAI has begun taking steps to plan an active role in this process. Its recent licensing agreement with Arizona State University included provisions that go beyond provision of services to create a "partnership" in which OpenAI will be involved in "designing, supporting and ensuring the effective use of its tools at ASU." Universities, of course, have significant interest in ensuring effective use of GAI, and it is easy to see how collaboration with OpenAI could benefit the university. Even so, universities and other stakeholders in higher education will need to consider how to make sure that they retain some measure of influence over how, when, and why to incorporate a technology with so much potential to disrupt institutional practices and norms, not to mention the meaning of learning and knowledge itself.

What we can see in the product landscape are glimpses of the individualized activities intrinsic to research and learning that the market is identifying as ripe for transformation.

Our Product Tracker has little to contribute to questions of this magnitude. However, what we can see in the product landscape are glimpses of the individualized activities intrinsic to research and learning that the market is identifying as ripe for transformation. Ethical debates in higher education about GAI usage are likewise often focused on assessing the pros and cons of very specific actions-whether using GAI to, say edit a paragraph for clarity is more acceptable than using it to generate a paragraph for subsequent editing. We will continue to benefit from engagement with action-level conversations about GAI usage to develop academic and research integrity standards and to pursue an understanding of which activities have unique value to learning and knowledge creation and which are "busy work."

At the same time, focusing too much attention on individual steps risks losing sight of learning and insight as holistic processes. We may be very comfortable with the widespread use of GAI to help us identify information, summarize it, or bring important passages to our attention, to generate comprehensive literature reviews capable of giving us the lay of the land in a fraction of the time it would take to map it ourselves, or to improve the internal organization and clarity of a research paper or assignment. But it is also necessary to think about the cumulative impact of mediating each of these activities together to assess how best to make GAI support teaching, learning, and research fit for the purpose of producing the insights required to address complex social and scientific challenges and foster critical and engaged students, citizens, and communities.

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Generative AI in Higher Education - Ithaka S+R

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Generative AI has quickly gained a significant foothold in academia, and is now used widely for teaching, learning, and research purposes. New products are appearing so rapidly that just keeping up with them is difficult, and understanding the value of individual products in a now-crowded marketplace is a major challenge for end users and for university CIOs, IT departments, and others involved in decision making about which products will be supported and/or licensed for campus users.