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UERU Advising Administration Community of Practice Working Group on AI

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UERU Advising Administration Community of Practice Working Group on AI

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A specialized higher education workgroup focused on AI in academic advising and student success. Members examine how AI can support advising, improve student services, and influence the future of advising administration. Participation is available through Universities for Educational Research and Understanding membership.

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Julia M. Spears, Ph.D., is the Assistant Provost of Online Education and Certification. She has oversight for online education, the Design Center, microcredentials and certification. She is the campus coordinator for Quality Matters (QM), State Authorization Network (SAN) and the WV Higher Education Policy Commission Virtual Learning Network (WVVLN). She also serves on the Strategic Enrollment Management committee. As a Certified Practitioner of Human-Centered Design, she will use design thinking strategies to shape the future direction of Marshall’s online programs.

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AACC AI Skills for All Community of Practice

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AACC AI Skills for All Community of Practice

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An American Association of Community Colleges and Microsoft initiative bringing together community college workforce leaders to share strategies for integrating AI skills into programs, faculty and staff development, and institutional operations. The community uses webinars, peer learning, expert speakers, practical tools, and shared examples. Participation appears to be coordinated through AACC member colleges rather than through a public individual application.

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AACC - Building a Nation of Learners by Advancing America’s Community Colleges

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AI Skills for All: Community Colleges Lead the Way - AACC

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AACC believes AI skilling is critical for individuals, communities, and employers across the United States. It is in demand both as a stand-alone academic discipline and relevant across every academic cluster as AI applications are deployed across all sectors. With funding from Microsoft, AACC is hosting webinars for all community colleges and leading a Community

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UPCEA AI Labs, Study Groups, and CORe Community

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UPCEA AI Labs, Study Groups, and CORe Community

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UPCEA offers targeted AI labs and study groups for enrollment leaders, instructional designers, administrators, and online education professionals. Participants collaborate with peers and create practical action plans. UPCEA’s CORe Community also supports ongoing discussion and knowledge sharing about AI in online and professional education. Membership or separate program enrollment may be required.

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UPCEA AI Hub: Applied AI for Online & Professional Education

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Strategically implement AI in higher education. Access UPCEA’s primers, research, and training for enrollment growth, student success, credential innovation, and institutional strategy.

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A higher education AI community offering online discussions, recordings, a member directory, and ongoing conversations through an AI-focused Slack channel. Topics include institutional AI implementation, governance, prompting, custom GPTs, AI-enabled workflows, marketing, and digital strategy. Registration for the group is free for current Digital Collegium members.

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For conversations about AI in higher education

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A community for women leaders, educators, and innovators working at the intersection of AI and education. Participants connect with peers, exchange practices, collaborate on projects, and participate in webinars and community events. The group includes educators from multiple sectors, including higher education.

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I invite you to a new community of women leaders and innovators in AI and education. We aim to bring together a diverse group of educators and leaders using AI to transform learning, improve teaching, and create more inclusive educational opportunities.

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Google for Education Higher Education Communities 

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Google for Education Higher Education Communities 

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An online discussion platform for higher education professionals with forums, events, curriculum resources, and role- and discipline-specific groups. Current conversations address AI assessment, cognitive off-loading, generative AI in university operations, instructional design, and AI use across academic disciplines.

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GSA Artificial Intelligence Community of Practice 

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GSA Artificial Intelligence Community of Practice 

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A cross-sector AI community hosted by the U.S. General Services Administration. People working in academia may join with a .edu email address. The group holds monthly virtual meetings with guest speakers, organizational showcases, training opportunities, and collaborative working sessions. https://www.gsa.gov/artificial-intelligence/ai-community-of-practice

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The Artificial Intelligence Community of Practice supports integrating AI into mission delivery through events, training, and exploration of enterprise AI tools.

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A large higher education community discussing the pedagogical, ethical, privacy, governance, and operational implications of generative AI for students, faculty, and staff. Members participate through EDUCAUSE Connect discussions, shared resources, and virtual meetings. The group currently includes more than 4,000 participants.

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HA! We're being left to fend for ourselves and figure it all out. But we're still being told from upper admins to "do something about it" and make sure the students are learning.

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We are developing short-term credentials and continuing education for employees who need to build AI-related skills.

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We are developing short-term credentials and continuing education for employees who need to build AI-related skills.

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We are teaching students to recognize inaccurate, biased, incomplete, or fabricated AI output.

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We are concerned that students may graduate without the AI skills employers increasingly expect.

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Our faculty are moving faster than our policies and infrastructure.

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At Lamar University, we are approaching AI as a teaching and learning, workforce, technology, and institutional strategy issue rather than simply an academic integrity concern. We are developing responsible-use guidance, supporting faculty and staff development, integrating AI literacy and discipline-specific applications into academic programs, redesigning learning and assessment, evaluating secure tools, and working with employers and community partners to understand regional workforce needs. We are also addressing privacy, accessibility, equity, cybersecurity, and human oversight as we move from individual experimentation toward a more coordinated institutional approach.

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We are addressing access, privacy, cybersecurity, accessibility, and interoperability before scaling AI tools.

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We are using AI to support employees, not simply to replace positions.

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We are using AI to support employees, not simply to replace positions.

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We are supporting adult learners, displaced workers, and incumbent employees who need AI upskilling.

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We are supporting adult learners, displaced workers, and incumbent employees who need AI upskilling.

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We are incorporating AI into career and technical education programs based on how it is being used in the workplace.

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We are working with employers to identify how AI is changing regional workforce needs.

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We are providing students with AI literacy training, including prompting, verification, ethical use, privacy, and disclosure.

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We are exploring and starting in how AI can support feedback, tutoring, practice, accessibility, and personalized learning.

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We are exploring and starting in how AI can support feedback, tutoring, practice, accessibility, and personalized learning.

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We offer workshops on AI-supported teaching, assessment redesign, course development, research, and administrative productivity.

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We are providing professional development to help faculty use AI effectively and responsibly.

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We are providing professional development to help faculty use AI effectively and responsibly.

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Several faculty and staff members are experimenting with AI, but we do not yet have a coordinated institutional strategy.

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We are still assessing how AI may affect teaching, learning, operations, and workforce preparation.

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An online program for higher education faculty, instructional designers, and support staff. It addresses AI foundations, curriculum integration, student engagement, personalized learning, assessment, and responsible implementation. Registration fees may apply.

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"Teaching with AI" is an engaging online program tailored for higher education faculty, instructional designers, and support staff designed to deepen

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A self-directed learning path covering AI foundations, large language models, generative AI, prompting, responsible use, and educational applications. It is identified for both K-12 and higher education educators and has no prerequisites.

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Explore the potential of artificial intelligence in education.

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A structured online course moving from foundational AI concepts to practical instructional implementation. It includes AI-supported learning design, assessment, academic integrity, and pedagogically appropriate classroom use. Registration fees apply.

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AI Deep Dive for Educators provides a complete introduction to artificial intelligence (AI) in education, guiding educators from foundational AI concepts…

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Anthology and UNESCO: Free AI Courses for Educators

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Anthology and UNESCO: Free AI Courses for Educators

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Offers modules on AI concepts, AI-supported course design, student engagement, and ethical practices. The courses include material connected to Anthology’s AI Design Assistant and are provided through UNESCO’s Global Skills Academy.

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Anthology, partner of UNESCO’s Global Skills Academy

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OpenAI Academy: Higher Education Community

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OpenAI Academy: Higher Education Community

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Provides webinars, replays, practical resources, and campus examples for university faculty, staff, researchers, and leaders. Topics include teaching, research, AI literacy, ChatGPT Edu, institutional implementation, and workspace agents. Registration may be required.

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Unlock the new opportunities of the AI era by equipping yourself with the knowledge and skills to harness artificial intelligence effectively.

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A higher education webcast hub covering AI, online learning, accessibility, course quality, institutional policy, student support, and digital learning. Many session pages provide recordings and supporting materials.

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WCET webcasts explore topics in digital learning and feature expert presenters.

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WCET: Advancing Quality in Digital Learning Through AI Tools

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WCET: Advancing Quality in Digital Learning Through AI Tools

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Features academic leaders discussing how AI can support course design, instruction, quality standards, rubrics, and faculty efficiency across disciplines and delivery formats.

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Advancing Quality in Digital Learning Through AI Tools

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WCET is the leader in the practice, policy, & advocacy of digital learning in higher education.

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Examines the opportunities and risks created when AI is incorporated into educational environments. The session is particularly relevant to institutions considering accessibility needs.

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Where AI Meets Accessibility: Considerations for Higher Education

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WCET is the leader in the practice, policy, & advocacy of digital learning in higher education.

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EDUCAUSE: AI Is Here. Are Students Still Learning?

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EDUCAUSE: AI Is Here. Are Students Still Learning?

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Addresses how colleges and universities can integrate AI without undermining rigor, critical thinking, creativity, or academic integrity. It also considers institutional policy, faculty practice, and preparation of students for AI-influenced workplaces.

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AI is Here. Are Students Still Learning? A Candid Conversation with Higher Ed Leaders

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EDUCAUSE: AI Potential, Higher Education’s Shift from Literacy to Fluency

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EDUCAUSE: AI Potential, Higher Education’s Shift from Literacy to Fluency

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Focuses on moving beyond introductory AI awareness toward institution-wide fluency. It is relevant to faculty development, curriculum planning, campus readiness, and preparing students to use AI effectively and responsibly.

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AI Potential: Higher Ed’s Shift from Literacy to Fluency

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EDUCAUSE: Learning and Teaching with AI Through plAI

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EDUCAUSE: Learning and Teaching with AI Through plAI

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Explores playful and creative educational uses of tools such as ChatGPT, Gemini, NotebookLM, image generators, and text-to-voice systems. It is designed for educators and instructional designers interested in games, experimentation, creativity, and interdisciplinary learning.

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Step into the magic circle of playful learning in this engaging webinar designed for educators and instructional designers. Explore how generative AI tools-such as ChatGPT, Gemini, NotebookLM, DALL·E, and a variety of text-to-voice and image-generation platforms-can be creatively harnessed to design educational games, spark playful interactions, and inspire innovation across disciplines.

While these tools are rapidly reshaping student practices, their potential for playful, curiosity-driven integration into academic settings remains largely untapped. Join Dr. Diane Sieber as she invites participants to explore, experiment, and reimagine learning experiences using AI-generated content. Discover how embracing play can deepen student engagement, fuel creative thinking, and transform the learning environment in bold and joyful ways.

Upon completion of this webinar, you will have learned to:

Identify best practices, ethical considerations, and a pedagogical framework for playfully integrating generative AI into university teaching.

Explore strategies for balancing play and academic rigor to foster student creativity, experimentation, and innovation.

Apply tools and reflection prompts to guide student discussions on AI strengths and limitations, responsible use, and the implications for human agency and authorship.

Presenters

Kim Arnold

Director, Teaching & Learning Program, EDUCAUSE

Diane Sieber

Faculty Director, Generative Futures Lab for AI, College of Engineering and Applied Science, University of Colorado Boulder

Resources & Downloads

AI Bingo

105 KB, pdf - Updated on 9/4/2025

Slides

3 MB, pdf - Updated on 9/4/2025

Accessibility

EDUCAUSE provides real-time AI captioning for all online events, in the main room and also in breakout rooms. To request any additional accommodations and/or services, please send an email to [email protected] or call (303) 449-4430 for assistance.

Virtual Event Policies and Expectations

Please review the EDUCAUSE Virtual Event Policies and Expectations for detailed information. View EDUCAUSE Virtual Event Policies and Expectations→

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Step into the magic circle of playful learning in this engaging webinar designed for educators and instructional designers.

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OpenAI Academy: The AI Educator Brain on Prompting

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OpenAI Academy: The AI Educator Brain on Prompting

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A recorded webinar focused on helping educators develop more effective prompts and use AI responses purposefully in educational settings. It is especially relevant to prompt literacy and instructional applications.

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The AI Educator Brain on Prompting: Mastering the Art of AI Responses in the Classroom

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Back by popular demand! Join Sari Beth Rosenberg, Kelly Booz and EdBrAIn—and a special guest, solutions engineer Mohammed Husain, from OpenAI—as we continue our journey into the art of prompting. Crafting the perfect prompt is like finding the secret password to unlock AI’s true potential in your classroom—whether it’s for lesson planning, engaging students, or streamlining administrative tasks. In this fun, hands-on webinar, we’ll explore:

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OpenAI Academy: Creating Workspace Agents for Higher Education Faculty and Researchers

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OpenAI Academy: Creating Workspace Agents for Higher Education Faculty and Researchers

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A webinar replay introducing workspace agents for recurring faculty and research workflows. It is most relevant to educators exploring more advanced AI use beyond conversational prompting.

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Creating Workspace Agents for Higher Ed Faculty and Researchers - Webinar Replay - Video | OpenAI Academy

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Unlock the new opportunities of the AI era by equipping yourself with the knowledge and skills to harness artificial intelligence effectively.

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Future Tools - Find The Exact AI Tool For Your Needs

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FutureTools Collects & Organizes All The Best AI Tools So YOU Too Can Become Superhuman!

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Provides a directory of community-created AI applications and interactive demonstrations. It is particularly useful for exploring open-source models, experimental tools, and applications that can be tested directly in a browser.

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Discover amazing ML apps made by the community

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Easy With AI is home to one of the largest collections of AI tools and resources on the internet. Find & search AI tools in over 50+ different categories.

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OpenFuture - The World-Largest AI Tools Directory in 2024

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OpenFuture is the most extensive AI tools library. Look at our directory with many AI tools in 40+ frequently used categories. Check out the best tools for you now!

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Kestin et al.: AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting

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Kestin et al.: AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting

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A randomized crossover study in introductory physics found higher immediate post-test scores and lower median time-on-task with a custom AI tutor than with two specific active-learning lessons. The findings are limited to this course and intervention.

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AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting - Scientific Reports

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Scientific Reports - AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting

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Pallant et al.: Mastering Knowledge: The Impact of Generative AI on Student Learning Outcomes

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Pallant et al.: Mastering Knowledge: The Impact of Generative AI on Student Learning Outcomes

# data[].attributes.body
Analyzed 192 student reflections across three marketing units. Students described both constructive, knowledge-building uses of AI and more procedural uses focused on completing tasks.

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Mastering knowledge: the impact of generative AI on student learning outcomes

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Generative AI (GenAI) has had a significant impact across industries since the launch of ChatGPT in late 2022. Much of the focus of existing research in the higher education space has considered th…

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Jin and Sercu: ChatGPT Interventions in Higher Education: A Systematic Review of Experimental Studies

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Jin and Sercu: ChatGPT Interventions in Higher Education: A Systematic Review of Experimental Studies

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Reviewed 21 experimental studies. ChatGPT interventions were generally associated with gains in knowledge acquisition, while findings for skill development were less consistent.

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Qian: Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field

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Qian: Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field

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Reviewed 37 empirical studies published in 2023 and 2024. The review identified uses involving creativity, critical thinking, autonomy, feedback, and prompt literacy, while also noting risks of cognitive outsourcing.

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Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field - TechTrends

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The release of ChatGPT in late 2022 marked the beginning of a rapid transformation in higher education, soon followed by the development of multimodal generative AI programs. As this technology becomes increasingly integrated into teaching and learning, it is crucial to evaluate its current use and impact. This systematic literature review captures the initial academic response to generative AI, providing insights into how higher education has adopted this transformative technology in its first two years. The findings indicate that while some themes from the pre-ChatGPT era persist, new and emerging trends—particularly in fostering creativity, critical thinking, learning autonomy, and prompt literacy—are now taking shape. This shift underscores a growing emphasis on the pedagogical integration of generative AI. However, the review also highlights a key tension: while generative AI enhances efficiency, it raises concerns about overreliance, potentially leading to the outsourcing of critical cognitive and metacognitive skills. To address these challenges and fully harness the potential of generative AI, future research should focus on exploring multimodal generative AI tools and fostering student–teacher-AI collaboration.

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Wei et al.: The Effects of Generative AI on Collaborative Problem-Solving and Team Creativity Performance in Digital Story Creation

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Wei et al.: The Effects of Generative AI on Collaborative Problem-Solving and Team Creativity Performance in Digital Story Creation

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In a 20-week study of 60 students, the AI-supported group showed stronger collaborative problem-solving and higher scores in some dimensions of team creativity. The study focused on digital storytelling.

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The effects of generative AI on collaborative problem-solving and team creativity performance in digital story creation: an experimental study - International Journal of Educational Technology in Higher Education

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As the demand for higher-order thinking skills continues to rise in the 21st century, the integration of Generative Artificial Intelligence (GAI) into educational practices has emerged as a promising tool. However, its full potential in enhancing collaborative problem-solving and team creativity within educational contexts, particularly in Digital Storytelling (DST), remains insufficiently explored. This study investigated the effects of GAI tools, including ChatGPT, Midjourney, and Runway, on university students’ Collaborative Problem-Solving (CPS) skills, Team Creativity Performance (TCP), and their perceptions of GAI’s role in DST creation. Employing a mixed-methods approach, the study utilized pre- and post-surveys along with semi-structured interviews to assess these outcomes. Over a 20-week DST training program, sixty university students were randomly assigned to either an experimental group creating their DST with the aid of GAI tools or a conventional group designed DST via traditional methods. Results indicated that the integration of GAI tools in DST creation significantly enhanced students’ CPS skills. Furthermore, the experimental group demonstrated better performance in team creativity, particularly in the user experience and novelty dimensions. It also revealed that while students generally held a positive attitude toward the use of GAI in DST creation, they also expressed concerns about potential cognitive offloading, over-reliance on GAI technology, and the lack of emotional support. This study offers valuable insights for educators and researchers seeking to incorporate GAI technologies into DST creation as a way to foster collaborative learning and creativity in educational settings.

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Hwang and Lee: Exploring Students’ Experiences and Perceptions of Human-AI Collaboration in Digital Content Making

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Hwang and Lee: Exploring Students’ Experiences and Perceptions of Human-AI Collaboration in Digital Content Making

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Compared students in a prompt-literacy course with students using conventional digital tools. The prompt-literacy group showed greater improvement in creative problem-solving, though the groups were not randomly assigned.

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Exploring students’ experiences and perceptions of human-AI collaboration in digital content making - International Journal of Educational Technology in Higher Education

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This study investigated university students’ experiences and perceptions of collaborating with generative AI for digital content creation. Participants engaged in a digital contest addressing sociocultural issues, using generative AI while emphasizing the modification and refinement of AI outputs, rather than direct submission from AI-generated content. To provide more scientific results, this study included two groups: an experimental group using generative AI (Prompt Literacy; PL) and a comparative group relying solely on conventional digital tools (Digital Literacy; DL). This study compared the groups in terms of their creative problem-solving abilities and curiosity. The findings indicated that the PL group showed significantly enhanced creative problem-solving skills compared to the DL group. Students in the PL group perceived generative AI as a valuable partner that helped in idea creation, increased efficiency, and more dynamic creative exploration, although some expressed concerns about its limitations and ethical issues. The results suggest that prompt literacy plays a critical role in understanding new human-AI interaction and collaboration, which highlights the importance of human agency facilitated by technological support. Based on these findings, this study suggests some pedagogical implications of integrating generative AI into educational tasks and highlights the potential of AI to foster emergent creativity.

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Farrokhnia et al.: Generative AI Offers More, but Students Revise Less: Comparing the Effects of Teacher and AI Feedback on Student Essay Revisions

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Farrokhnia et al.: Generative AI Offers More, but Students Revise Less: Comparing the Effects of Teacher and AI Feedback on Student Essay Revisions

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A randomized study compared teacher feedback with two forms of AI feedback. More highly rated AI feedback did not lead to significantly greater essay improvement.

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Generative AI offers more, but students revise less: comparing the effects of teacher and AI feedback on student essay revisions - International Journal of Educational Technology in Higher Education

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Providing high-quality feedback on student writing is essential yet increasingly difficult due to rising class sizes and limited instructional capacity. Generative AI (GenAI) offers a promising and scalable alternative, but its effectiveness compared to traditional teacher feedback, particularly across different prompting techniques, remains uncertain. This study employed a quantitative, randomized three-group experimental design with 70 graduate students to compare the effects of teacher feedback and GenAI feedback generated using two prompting techniques: Zero-shot and chain-of-thought (CoT). The study explored how these feedback sources affect feedback quality and students’ uptake during essay revision. It involved a two-stage process in which students first wrote an argumentative essay and then revised it based on the feedback received. Feedback and essay quality were evaluated using a rubric based on Toulmin’s model of argumentation and analysed using inferential statistical methods. Results showed that CoT prompting produced higher quality feedback than both Zero-shot prompting and teacher feedback, suggesting that stepwise reasoning in CoT aligns GenAI outputs more closely with the cognitive demands of argumentative writing. However, this higher feedback quality did not lead to significantly greater improvements in revisions. Teacher feedback, although rated lower in quality, resulted in comparable gains in essay quality. In addition, GenAI feedback quality was significantly associated with students’ initial essay quality, whereas teacher feedback quality showed no such association. These findings indicate that feedback quality alone is insufficient to enhance writing outcomes; rather, students’ engagement with and uptake of feedback play a critical role. Overall, the results highlight the potential of hybrid intelligent feedback systems in which teachers support students in interpreting and applying GenAI feedback to meaningfully improve their writing.

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Henderson et al.: Comparing Generative AI and Teacher Feedback: Student Perceptions of Usefulness and Trustworthiness

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Henderson et al.: Comparing Generative AI and Teacher Feedback: Student Perceptions of Usefulness and Trustworthiness

# data[].attributes.body
Surveyed 6,960 students at four Australian universities. Students generally viewed teacher feedback as more helpful and trustworthy, while valuing AI feedback for speed, accessibility, and volume.

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Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness

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The rapid integration of Generative Artificial Intelligence (GenAI) into educational contexts has presented both opportunities and challenges for students seeking and using feedback. While AI-gener…

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Zhan and Yan: Students’ Engagement with ChatGPT Feedback: Implications for Student Feedback Literacy

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Zhan and Yan: Students’ Engagement with ChatGPT Feedback: Implications for Student Feedback Literacy

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Studied 16 undergraduates using ChatGPT feedback on IELTS writing tasks. Students often engaged more cognitively than metacognitively and varied in how critically they evaluated and applied the feedback.

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Students’ engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence

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Student feedback engagement is often reported as unsatisfactorily low, undermining the effectiveness of feedback in higher education. The emergence of Generative Artificial Intelligence (GenAI) has…

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Walton et al.: How University Students Work on Assessment Tasks with Generative Artificial Intelligence: Matters of Judgement

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Walton et al.: How University Students Work on Assessment Tasks with Generative Artificial Intelligence: Matters of Judgement

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Used interviews and students’ prior AI interaction records to examine decision-making during assessments. The study identified both critical evaluation and uncritical reliance on AI-generated content.

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How university students work on assessment tasks with generative artificial intelligence: matters of judgement

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Despite concerns about students’ use of generative AI (GenAI) in assessment, the technology has become embedded into students’ everyday assessment practices. It is unclear how students are making j…

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Corbin et al.: “Where’s the Line? It’s an Absurd Line”: Towards a Framework for Acceptable Uses of AI in Assessment

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Corbin et al.: “Where’s the Line? It’s an Absurd Line”: Towards a Framework for Acceptable Uses of AI in Assessment

# data[].attributes.body
Interviewed 19 students and 12 staff members at one Australian university. Participants reported difficulty defining and communicating clear boundaries for acceptable AI use in assessment.

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‘Where’s the line? It’s an absurd line’: towards a framework for acceptable uses of AI in assessment

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As higher education grapples with ensuring assessment validity in an increasingly AI-populated time, institutions and educators are working to establish appropriate boundaries for AI use. However,…

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Lee and Palmer: Prompt Engineering in Higher Education: A Systematic Review to Help Inform Curricula

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Lee and Palmer: Prompt Engineering in Higher Education: A Systematic Review to Help Inform Curricula

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Reviews prompt-engineering frameworks relevant to higher education. The authors emphasize connecting prompts to context, educational goals, and evaluation of AI outputs.

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Prompt engineering in higher education: a systematic review to help inform curricula - International Journal of Educational Technology in Higher Education

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This paper presents a systematic review of the role of prompt engineering during interactions with Generative Artificial Intelligence (GenAI) in Higher Education (HE) to discover potential methods of improving educational outcomes. Drawing on a comprehensive search of academic databases and relevant literature, key trends, including multiple framework designs, are presented and explored to review the role, relevance, and applicability of prompt engineering to purposefully improve GenAI-generated responses in higher education contexts. Multiple experiments using a variety of prompt engineering frameworks are compared, contrasted and discussed. Analysis reveals that well-designed prompts have the potential to transform interactions with GenAI in higher education teaching and learning. Further findings show it is important to develop and teach pragmatic skills in AI interaction, including meaningful prompt engineering, which is best managed through a well-designed framework for creating and evaluating GenAI applications that are aligned with pre-determined contextual educational goals. The paper outlines some of the key concepts and frameworks that educators should be aware of when incorporating GenAI and prompt engineering into their teaching practices, and when teaching students the necessary skills for successful GenAI interaction.

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Wessels: Developing an AI Framework for Learning in Higher Education: A Humanities Perspective from English Literature

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Wessels: Developing an AI Framework for Learning in Higher Education: A Humanities Perspective from English Literature

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Examines the use of AI tools to support students studying Chaucer. AI helped with initial language and context but lacked the depth needed for advanced literary study.

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Developing an AI framework for learning in higher education: a humanities perspective from English Literature - International Journal of Educational Technology in Higher Education

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Little is known about how to develop frameworks for using AI to support learning in English Literature within higher education. This paper presents research that developed a framework for employing AI in the study of the fourteenth-century English poet Geoffrey Chaucer. Studying Chaucer presents challenges for modern readers, as it requires an understanding of his literary style, spoken Middle English, and the cultural and social context of the late medieval period. The research investigated how AI tools, such as ChatGPT and Amazon Polly, could support the study of Chaucer. It found that while AI can assist with students’ initial familiarisation with Chaucer’s poetry, it lacks the depth and rigour necessary for more advanced study. As a result, students and lecturers still rely on traditional resources, including authoritative print-based and digital scholarly editions. Consequently, the research developed a framework for studying Chaucer’s poetry that integrates the guided use of AI through active learning with additional supporting resources, such as digital scholarly editions.

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Kim et al.: Examining Faculty and Student Perceptions of Generative AI in University Courses

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Kim et al.: Examining Faculty and Student Perceptions of Generative AI in University Courses

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Surveyed 982 students and 76 faculty members at a large U.S. public university. The study identified differences in reported use, perceived usefulness, ethical concerns, and intentions across groups and disciplines.

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Examining Faculty and Student Perceptions of Generative AI in University Courses - Innovative Higher Education

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As generative artificial intelligence (GenAI) tools such as ChatGPT become more capable and accessible, their use in educational settings is likely to grow. However, the academic community lacks a comprehensive understanding of the perceptions and attitudes of students and instructors toward these new tools. In the Fall 2023 semester, we surveyed 982 students and 76 faculty at a large public university in the United States, focusing on topics such as perceived ease of use, ethical concerns, the impact of GenAI on learning, and differences in responses by role, gender, and discipline. We found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies. Students and instructors also used GenAI for coursework or teaching at similar rates, although regular use of these tools was still low across both groups. Among students, we found significant differences in attitudes between males in STEM majors and females in non-STEM majors. These findings underscore the importance of considering demographic and disciplinary diversity when developing policies and practices for integrating GenAI in educational contexts, as GenAI may influence learning outcomes differently across various groups of students. This study contributes to the broader understanding of how GenAI can be leveraged in higher education while highlighting potential areas of inequality that need to be addressed as these tools become more widely used.

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Heil et al.: Students’ Perceived Impact of GenAI Tools on Learning and Assessment in Higher Education: The Role of Individual AI Competence

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Heil et al.: Students’ Perceived Impact of GenAI Tools on Learning and Assessment in Higher Education: The Role of Individual AI Competence

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Surveyed 223 students from institutions in multiple countries. Students’ self-reported AI competence was associated with how they evaluated the usefulness of different generative AI tools.

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Students’ perceived impact of GenAI tools on learning and assessment in higher education: the role of individual AI competence - Smart Learning Environments

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The adoption of artificial intelligence (AI) and generative artificial intelligence (GenAI) tools in higher education institutions (HEIs) raises numerous questions and application possibilities. This study included N = 223 students from HEIs across different countries and investigated students’ AI competence and how they evaluated the specific benefits of several GenAI tools for learning and assessment. The GenAI tools included in the study were designed for different application contexts and purposes. The study goes beyond students’ preferences by simultaneously adopting a broad approach that considers their AI competence, their perspectives on six different GenAI tools, and a focused approach that investigates the specific benefits of these tools in learning and assessment. The results indicate that the dimensions of AI competence vary considerably, significantly impacting how the GenAI tools were evaluated. Results also show that students perceived and evaluated the different tools according to their potential use in pursuing their study goals. This research calls for a more nuanced and differentiated analysis of AI approaches by different stakeholders in HEIs for the promotion of the enhancement of students’ AI competence and awareness in using GenAI tools rather than amalgamating AI and GenAI tools under one banner and for understanding possible benefits and application of them in HE learning and assessment processes.

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Designed for faculty developers, instructional designers, online educators, and academic leaders. Resources are organized around planning, developing, and evaluating AI-supported online teaching and learning.

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Overview This AI Resource Hub covers AI use organized into Planning, Developing, and Evaluating sections to support the integration of artificial intelligence in online teaching and learning. The resources curated here help faculty developers, instructional designers, and academic leaders make informed decisions about incorporating AI in ways that enhance instruction, uphold academic integrity, and align … Continue reading “TOPkit AI Resource Hub”

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Provides an extensive faculty resource collection covering AI fundamentals, ethical use, academic integrity, assignment design, assessment, accessibility, course policies, and instructional examples.

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In Spring 2023, the Cornell administration assembled a committee to develop guidelines and recommendations for the use of Generative AI for education at Cornell. Their final report evaluates the feasibility, benefits, and limitations of using generative AI technologies in an educational setting and its impact on learning outcomes. Read the CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy

Since the release of new generative artificial intelligence (AI) tools, including ChatGPT, we have all been navigating our way through both the landscape of AI in education and its implications for teaching. As we adapt to these quickly evolving tools and observe how students are using them, many of us are still formulating our own values around what this means for our classes.

Our CTI resources aim to provide support on what these tools are and how they work. We'll address common concerns and considerations in the context of AI, such as academic integrity, accessibility and ethical uses of the technology, and we'll explore practical applications and pedagogical strategies for teaching and assignment design as you determine what approaches and policies regarding AI are the right fit for your classes.

What is Generative Artificial Intelligence (AI)?

Generative artificial intelligence is a subset of AI that utilizes machine learning models to create new, original content, such as images, text, or music, based on patterns and structures learned from existing data. A prominent model type used by generative AI is the large language model (LLM).

An LLM, like ChatGPT, is a type of generative AI system that can produce natural language texts based on a given input, such as a prompt, a keyword, or a query. LLMs typically consist of millions or billions of parameters that are "trained" on massive amounts of text data, such as books, articles, websites, and social media posts, and can perform various tasks, such as answering questions, summarizing texts, writing essays, creating captions, and generating stories. LLMs can also learn from their own outputs and are likely to improve over time.

It's important to note that while LLMs can answer questions and provide explanations, they are not human and thus do not have knowledge or understanding of the material they generate. Rather, LLMs generate new content based on patterns in existing content, and build text by predicting most likely words.

Because of how LLMs work, it is possible for these tools to generate content, explanations, or answers that are untrue. LLMs may state false facts as true because they do not truly understand the fact and fiction of what they produce. These generated fictions presented as fact are known as "hallucinations."

How Will Generative AI Affect Higher Education?

Nobody knows the true impact that generative AI will have on higher education. These technologies are rapidly evolving in complexity and type of use. What we do know is that generative AI is opening up a world of possibilities, while also generating significant concerns about academic integrity, ethics, access and bias.

Before we dig too deep into whether and how to incorporate generative AI into your courses, here are a few general steps you can take as you consider what generative AI means for your classroom:

Reflect: How do you feel about generative AI? Concerned? Excited? A little of both? What additional information do you need to feel able to make informed decisions about whether or not to incorporate it into your courses?

Try it out: Experiment with generative AI platforms relevant to your discipline, like ChatGPT, Bard, or DALL-E 2. Choose a tool, then ask it to complete an assignment you'd give your students. What are the results? Ask it to revise the assignment, and see how it responds. Can you identify possible areas of concern for academic integrity, or opportunities for student learning?

Predict and inquire: How might students use this technology in your course? Can you ask students how they are currently using generative AI tools? What clarity will students need to distinguish between appropriate and inappropriate uses of these tools? Consider how you might adjust assignments to either incorporate generative AI into your course, or to identify areas where students may lean on the technology, and turn those hot spots into opportunities to encourage deeper and more critical thinking. How might you use these tools to assist your teaching? For example, could generative AI help generate practice problems for students?

Learn more: This technology is evolving, and none of us are experts yet. Be open to continuing to learn more and to having ongoing conversations with colleagues, your department, people in your discipline, and even your students about the impact generative AI is having.

Set your parameters: Decide whether and when you want students to use the technology in your courses, and clearly communicate your parameters and expectations with them. Keep in mind that students will likely have instructors with different ideas about how to use, or why or why not to use, generative AI tools. Be transparent and direct about your expectations.

The Upside: Possibilities for Generative AI to Benefit Learning Environments

We all want to discourage students from using generative AI to complete assignments at the expense of learning critical skills that will impact their success in their majors and careers.

However, we'd also like to take some time to focus on the possibilities that generative AI presents. Here are a few ways it can be useful to students and faculty alike:

Generative AI can potentially be used by both faculty and students to:

Provide instant access to vast amounts of information quickly

Aid diverse learners with different learning abilities, linguistic backgrounds or accessibility needs

Accelerate exploration and creativity, spark curiosity, suggest new ideas and ways of thinking

Students might explore using Generative AI to:

Be more efficient with course work and tasks

Help with studying

Generate ideas for brainstorming

Get further explanation of a topic a teacher is covering for class

Improve their writing

Get instant feedback

Practice language skills in a safe environment

Faculty might explore using Generative AI to:

Act like a virtual teaching assistant that helps with answering student questions, provides clarification, or offers guidance when not in class

Generate content and course materials including lesson plans, quiz questions, sample problems, or writing scenarios

Assist in research tasks including analyzing large datasets, identifying patterns, and generating insights and research directions

Write learning objectives, course descriptions, syllabi statements, or course policies

As you and your students prepare to investigate the use of generative AI tools, we recommend discussing course policies and expectations around their use, and clearly communicating with your students when and in what ways use of generative AI tools are permitted - or not. We also recommend that you consider the accessibility of generative AI tools as you explore their potential uses, especially those that students may be required to interact with. Finally, it's important to take into account the ethical considerations of using such tools. These topics are fundamental if considering using AI tools in your assignment design.

Generative AI Literacy

While ChatGPT and other LLMs can assist learners in various tasks and activities, they cannot replace human creativity, judgment, ethics, or responsibility, all of which are essential for learning. LLMs may help a learner write a paper or a report, but they cannot teach the learner how to conduct original research, synthesize information from multiple sources, formulate arguments, express opinions, or cite sources properly. Thus, the need for AI literacy is essential for students and faculty alike.

We can think of ethical generative AI literacies as the ability to understand, evaluate, and critically engage with generative AI technologies. Generative AI literacy includes skills such as recognizing when and how generative AI is used in various domains; assessing the reliability and validity of AI-generated outputs; identifying the ethical and social implications of AI applications; and creating and communicating with generative AI systems in ways that are appropriate to your course.

Just as we adapt to the changing media environment, developing AI literacy will be an ongoing process, but one that is vital to helping you and your students become more informed and responsible users and creators of AI technologies.

Stay Engaged and Informed

The range of AI applications and their abilities continue to develop rapidly, bringing both opportunities and challenges for educators wanting to stay current and informed. As the higher Ed landscape changes with the advent of this new technology, CTI aims to be a dependable partner and resource for faculty working to incorporate generative AI into their courses. Our goal is to support faculty in enhancing their teaching and learning experiences with the latest AI technologies and tools. As such, we look forward to providing various opportunities for professional development and peer learning.

As you further explore, you may be interested in CTI's generative AI events. If you want to explore generative AI beyond our available resources and events, please reach out to schedule a consultation.

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In Spring 2023, the Cornell administration assembled a committee to develop guidelines and recommendations for the use of Generative AI for education at Cornell. Their final report evaluates the feasibility, benefits, and limitations of using generative AI technologies in an educational setting and its impact on learning outcomes.

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Columbia University: Teaching and Learning in the Age of AI

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Columbia University: Teaching and Learning in the Age of AI

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Offers a broad collection of practical resources, including AI fundamentals, syllabus guidance, assignment redesign, prompt libraries, faculty use cases, disciplinary examples, accessible course design, and methods for evaluating learning.

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Want to navigate AI in your classroom with confidence? Columbia’s CTL is here to support you and your purposeful exploration of AI in education.

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University of Michigan: Generative AI Resources for Faculty and Instructors

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University of Michigan: Generative AI Resources for Faculty and Instructors

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Provides free guidance on getting started with generative AI, course policies, assignment redesign, academic integrity, responsible use, instructional strategies, and AI-augmented learning environments.

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U-M Guidance for Faculty/Instructors | U-M Generative AI

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The Getting Started with Generative Artificial Intelligence U-M Instructor Guide provides an overview and resources related to Generative Artificial Intelligence (GenAI or GAI).

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Stanford University: Artificial Intelligence Teaching Guide

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Stanford University: Artificial Intelligence Teaching Guide

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Helps instructors make intentional decisions about AI in courses. The guide includes AI literacy, course policies, assignment design, responsible use, student communication, and strategies for integrating AI into learning activities.

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A series of instructional web pages for instructors and teaching teams about using AI tools for teaching and learning.

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University of Michigan: Course and Assignment (Re-)Design

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University of Michigan: Course and Assignment (Re-)Design

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Encourages instructors to begin with course objectives and the rationale behind them, then determine which tasks students must personally complete to demonstrate learning. It emphasizes incremental development, authentic learning, higher-order thinking, collaboration, research, and sustainable experimentation rather than reacting to AI with isolated restrictions.

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We cannot fully anticipate how GenAI will impact every course or assignment at U-M. We will need to approach GenAI with an experimental mindset, draw on what we know, prioritize high-impact pedagogical strategies, and plan for incremental, sustainable instructional development. High-impact pedagogy includes collaborative learning, interactive learning, higher-order thinking, service- or community-based learning, research, authentic learning and assessment, and writing-intensive courses. Writing-intensive courses present particular benefits, opportunities, threats, and risks.

To begin this process, we recommend that instructors practice using GenAI tools relevant to their disciplines, interests, and pedagogies-before the start of fall term.

In preparation for fall term, instructors should evaluate several aspects of their courses:

1. What are the course objectives and rationale for them? Can GenAI be used to meet any of the objectives?

2. Are there new learning objectives in the areas of knowledge, skills, or values about GenAI that students need to meet? Will students have equitable access to GenAI for these objectives?

3. What tasks do students need to complete to demonstrate they meet the learning objectives?

4. How will learning be assessed?

5. Do the objectives, tasks, or assessments present problems with respect to equity, inclusion, diversity, or accessibility? If so, can they be adjusted to ensure fairness and inclusion?

The course design should include some parts that cannot be completed satisfactorily (solely) by GenAI tools, unless GenAI skills and values are the primary learning objectives. GenAI offers us an opportunity to rethink learning objectives as well as assessment methods. Several principles of effective pedagogy should guide us in adjusting courses to GenAI environments:

Larger component of technology-free in-class assignments
Including low-stakes writing, programming logic, etc. may be possible, while recognizing that in-class assignments can create inequities for some students with disabilities and English-language learners, unless appropriate accommodations are offered.

Focus on higher-order thinking
Require students to develop their capacity for creating, evaluating, analyzing, and applying (what they learned to new contexts or problems)

Emphasize the learning process over the product when appropriate
Assign scaffolded tasks developed through several stages (low-stakes writing, outlines, proposals, outlines, drafts, peer review, revision)

Prioritize authentic instruction and assessment
Assign tasks that address real-world problems or simulate real-world tasks; require students to use disciplinary theories and methods in innovative ways; ask students to address complex questions that have no easy or right answer; provide formative feedback that improves students' performance

Require accurate and verifiable citation of sources
Have students include links to the sources they use in a project (GenAI cannot reliably do this)

Add metacognitive exercises to assignments
Assign reflections about how formative feedback is understood and what was revised in response to feedback

Teach academic integrity
In designing assignments, we should explain the purpose of the task to foster motivated learning. The purpose should align with learning objectives in the areas of knowledge, skills and values:

1. what do students need to learn and why-what the value of the learning is for them now and/or in the future?

2. What skills will students gain from using AI, what knowledge they will need to apply.

Instructors are encouraged to try completing assignments using GenAI tools before distributing assignments to students. GenAI output can productively be used in class as a prompt for discussion about what technology does well and not so well, as well as the potential social risks and harms implicit in the output (e.g., racial bias, misinformation).

The following decision tree may be used to evaluate assignments in relation to GenAI environments:

IMAGE CREDIT: Frederique Laubepin

Instructors may find it useful to consider the principle of "transparent assignment design" as they review the relationship between course learning objectives and assignments. A transparent assignment template created by Mary-Ann Winkelmes at the University of Illinois offers a concise and concrete example.

Instructors may decide to assess learning with a wider range of assignments than they employed before sophisticated GenAI tools became widely available. The following strategies may prove useful.

Strategies for Engagement and Assessment

In line with best practices for teaching and learning, strategies for engaging and assessing students beyond recall and procedural, superficial understanding are effective pedagogical measures for rising above GenAI tools. Diverse teaching strategies align with diverse approaches to assessment. The following chart offers suggestions for instructors to consider as they redesign courses for GenAI environments.

Active, experiential, and project-based learning sequences similar to those encountered in the discipline

In-class polls, products of peer collaboration, self-assessment

Flipped classrooms to develop interactive learning, peer collaboration, and problem solving skills

In-class polls, products of peer collaboration

Reflections on learning and research processes

Assign a description of research methods, evaluation of sources, explanation of how sources contributed to students' thinking, and discussion of problems encountered and solved in the process.

Evaluate on the basis of completeness.

Live debates, oral presentations, role playing

Evaluation rubric co-created with students before the performances.

Evaluate on the basis of completeness.

Multimodal compositions, such as infographics, podcasts, drawings, websites, videos, alternative text creation for videos and images

Evaluation rubric co-created with students during scaffolded composition process

Authentic group, individual, and self-assessments that require critical and creative thinking

Social annotation of reading

Peer review of annotation quality

Requirements for students to contextualize information, make connections in written work to their lives, current events, and earlier course concepts or lectures, and/or incorporate required readings, videos, images, case studies, or datasets

Evaluate on the basis of completeness and successful integration/synthesis of course materials, concepts, theories, or methods

Poster displays and discussions

Peer review according to a rubric co-created with students during the learning process

Grading standards should be explicit when assignments are distributed and should prioritize higher-order thinking, veracity, critical and creative thinking, and learning processes over mechanical or grammatical correctness. The rationale for these priorities is that developing the human capacity for sentience, creativity, and reasoning aligns with the social value of higher education, while "correctness" (within narrow limits) can increasingly be accomplished by machines.

Integrating AI tools into Course Design

Teaching in an AI-augmented world will demand a reevaluation of the course design and pedagogical approaches. AI can be incorporated into a course in several ways. Examples include providing personalized learning experiences, predictive analytics to identify student progress towards learning goals, and automated grading systems. While integrating AI, it is important to critically examine:

1. Appropriate use of AI
Ensure that AI is used in a way that supports the learning objectives of the course.

2. Necessity of AI
While AI can improve various aspects of teaching and learning, it is crucial to discern if its implementation is necessary for a particular task or if a non-technological approach would be equally or more effective.

3. Pros and Cons of AI
Understand the benefits, such as increased efficiency and personalized learning, against the potential risks and drawbacks such as privacy issues, technology dependency, or reducing human interaction.

4. Avoiding the Tail Wagging the Dog Scenario
It is essential to ensure that AI serves as a tool to achieve educational objectives and not the other way round where the curriculum is altered to fit the AI tools.

5. Book Guide - Using Chatbots in Classroom Assignments and Activities
A TTC-created guide that highlights ideas for using chatbots in classroom assignments and activities. The guide is based on the ChatGPT Assignments to Use in Your Classroom Today book from the University of Central Florida.

Skills and Competencies for an AI-Augmented World

The future workforce will necessitate a unique blend of skills and competencies to thrive in an AI-driven environment. Accordingly, regardless of the field of study students should be exposed to the following aspects of GenAI:

1. Digital Literacy
Students should understand how AI systems work and appreciate the intricacies of machine learning, data analysis, and algorithm design.

2. Ethical Understanding
As AI systems become more complex, issues around privacy, bias, and ethics will be prominent. Students need to be aware of these ethical considerations and equipped with the skills to make informed decisions based on their values.

3. Critical Thinking and Problem Solving
While AI can automate many tasks, the ability to think critically and solve complex problems is something that cannot be replicated in the near future by machines. The cognitive dimension of learning is more important than ever, given the necessity of evaluating, fact-checking, and verifying AI output, as well as protecting against and mitigating the risks and harms of AI. These will remain key skills in the near-future workforce.

4. Adaptability and Lifelong Learning
AI is a rapidly evolving field. Therefore, the ability to adapt to new technologies and a commitment to lifelong learning will be crucial.

In conclusion, while AI can greatly augment the teaching and learning process, it is critical to maintain the delicate balance between leveraging technology and nurturing the unique human skills that AI cannot replicate. Doing so can prepare our students for an AI-augmented world without compromising on the essence and social value of education.

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We cannot fully anticipate how GenAI will impact every course or assignment at U-M. We will need to approach GenAI with an experimental mindset, draw on what we know, prioritize high-impact pedagogical strategies, and plan for incremental, sustainable instructional development.

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MIT Teaching + Learning Lab: Generative AI and Your Course

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MIT Teaching + Learning Lab: Generative AI and Your Course

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Connects generative AI decisions with established course-design principles. The resource helps instructors clarify intended learning outcomes, decide where AI supports or interferes with those outcomes, and align policies, activities, assessments, and student communication.

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How might generative AI prompt us to reconsider and refine goals for student learning?

Over the years, our ideas have changed regarding what students need to know how to do "from scratch" and what they can outsource to technology. For example, many courses permit (encourage) the use of software packages for mathematical operations and/or the analysis of scientific and engineering processes (e.g., Wolfram Alpha, Matlab, etc.) and the use of existing pieces of code (e.g., GitHub, etc.) in programming. And, of course - there is the calculator.

Before considering the affordances or annoyances of generative AI in your teaching context, it is important to critically examine your real goals for student learning. Are there levels of higher-order thinking - more complex, more authentic learning goals - that you'd like students to achieve? If so, you can begin to explore how generative AI tools help students achieve those goals.

In particular, you may wish to:

Engage with GenAI and examine how it handles your current assignment prompts and problems. Think back to your ideal goals for student learning and consider how you can modify your assignments to support your actual goals for student learning.

Consider how you might (re)design your assignments and/or course format to leverage generative AI and better support meaningful student learning.

Consider how GenAI can help you leverage the science of learning in your teaching. See TLL's post, Applying the Science of Learning in your Teaching: Generative AI May Help.

Teaching + Learning Lab Staff are available to help you refine your goals. Contact us at tll@mit.edu.

AI Acceptable Use Statements

Regardless of your thoughts on using GenAI in your subject, convey those thoughts, the resultant subject policies, and the consequences of their violation with your students at the beginning of the semester. Including an AI Acceptable-use Statement in your syllabus and discussing your rationale for the policy on the first day of class can help regulate students' use of GenAI and open the door for additional conversations about GenAI and learning in your subject as the semester progresses. If you choose to permit using GenAI for assignments, clearly specify how you expect students to cite and document its use.

Note that OpenAI's terms of use include the following among its restrictions: users may not "represent that output from the Services was human-generated when it is not." For guidelines on properly attributing GenAI output, see: How to Cite ChatGPT.
Teaching + Learning Lab Staff are available to help you develop an AI Acceptable Use statement for your syllabus. Additional resources are provided below.

TLL's February Post: Teaching & Learning with ChatGPT: Opportunity or Quagmire? Part III - Academic Integrity | Student Privacy | Equity & Accessibility includes three examples of AI Acceptable-use Syllabus Statements:

A statement that prohibits its use;

A statement that permits use with proper citation; and

A statement that allows use on a case-by-case basis.

Derek Bruff's Intentional Teaching newsletter

Lance Eaton's compilation of crowd-sourced policy statements

Torrey Trust's articles in Faculty Focus

As you rethink your assignments and assessments in light of genAI, keep your revised goals for student learning in mind. Consider whether you are hoping to leverage genAI's affordances or mitigate its use.

Writing in his blog, Agile Learning, Derek Bruff suggests that instructors ask themselves the following questions when considering an assignment revision:

Why does this assignment make sense for this course?

What are specific learning objectives for this assignment?

How might students use AI tools while working on this assignment?

How might AI undercut the goals of this assignment? How could you mitigate this?

How might AI enhance the assignment? Where would students need help figuring that out?

Focus on the process. How could you make the assignment more meaningful for students or support them more in the work?

Mitigating the Use of GenAI

For many instructors, thinking about the process of student learning and the assessment of that process is a useful way to (1) help students develop the habits of mind and skills essential to the discipline (or subject) and (2) shift the focus of student learning assessment away from end products that may lend themselves to genAI reliance and plagiarism.

Helping students to engage with the learning process may be particularly relevant here at MIT, where developing students as agile critical thinkers and problem solvers are primary and essential goals of an MIT education and cornerstones of the campus ethos. Experts in a field are fluent in the problem-solving process. They are comfortable "playing" with multiple solution paths and ideas - i.e., hitting dead ends - and learning from these mistakes to eventually formulate solutions. Many novices (our students included) believe if they don't see the solution right away, that they will fail. Learning how to solve problems involves learning from failed solution attempts and accepting that initial "failure" is almost always part of developing a successful solution. Here, a focus on the process, in addition to the product, can help students achieve our goals for them as MIT graduates and minimize chatbot plagiarism. (For more on expert v. novice learners, see the Resources section below.)

Higher education author and consultant John Warner recently commented: " One of the hallmarks of growing sophistication as a writer is seeing the idea you thought you were expressing change in front of your eyes as you are writing. This is high-level critical thinking. This kind of emergent rethinking is an experience that every college-level writer should be familiar with. " (Warner, 2022).

And, as Nancy Gleason, director of the Hilary Ballon Center for Teaching and Learning at NYU Abu Dhabi, wrote in The Times Higher Ed, "[...the assessment of only] a completed product is no longer viable. Scaffolding [and assessing] the skills and competencies associated with writing, producing and creating is the way forward." (Gleason, 2022).

Consider the usefully prescient comments of cognitive and learning scientist Michelene Chi in her 1994 paper on the impact of self-explanations during problem solving on the improvement of student science understanding: " ...especially for challenging science domains....students should learn to be able to talk science (to understand how the discourse of the field is organized, how viewpoints are presented, and what counts as arguments and support for these arguments), so that students can participate in scientific discussions, rather than just hear science. " (Chi, 1994)"

In subjects that use problem sets, ask students to explain their thought processes as they solve (a subset of) the problems.

A few (of many possible) helpful prompts may include asking them to describe:

Why they chose a particular method;

Why they made certain assumptions and/or simplifications;

Where they ran into dead ends and how they found their way forward;

What broader takeaways did they learn from solving the problem?

As a follow-up to any type of assignment, you can ask them to reflect on and articulate:

Any issues they had in beginning the assignment;

What they found most interesting or surprising;

Their "aha moment";

How the completion of the assignment added to their understanding of the topic, etc.

Developing students' metacognitive skills by requiring them to self-regulate and self-explain their solution process may mitigate their use of AI-generated responses. Self-evidently, it is much more difficult for students to explain their problem-solving process when they didn't actually solve the problem! If a student uses generative AI in some aspect of the solution, the requirement that they document their thought processes will force them to engage a bit deeper with certain aspects of the problem and the learning process overall.

Frequent Low-stakes, In-class Quizzes

Regardless of your assignment types (psets, weekly papers, reading reflections, coding), consider adding weekly, low-stakes, in-class quizzes that ask them to solve a close variant of one or two of the pset questions from the week and/or a previous week; reflect on and/or summarize a discussion from a previous class.

Frequent, low-stakes quizzing allows students to practice retrieval - a key component of the learning process, and if the quizzes pull questions from previous weeks, it can further develop students' ability to retrieve and apply recently learned concepts and skills. See TLL's How to Teach pages for additional information on retrieval and spaced & interleaved practices.

If you use this strategy, you'll want to:

Be explicit with students about what you are doing and why.

Include follow-up questions from the previous section of the course (above) in your pset - to encourage students to reflect on the problem-solving process.

During the first few weeks of the semester, model the type of reflective and engaged responses that you are looking for on the quizzes.

Reconsider how you allocate points for the various assignments in your subject. With the addition of weekly, in-class quizzes - you may want to decrease point value and/or the grading time required for other assignments.

Leveraging GenAI

As noted in the Refine Your Goals for Learning section, the affordances of GenAI may allow you to better support students' development of higher-order, more complex skills like synthesis, analysis, and creation.

In their paper, Mollick and Mollick offer detailed descriptions of ways to leverage programs like ChatGPT in student assignments. They suggest that " ...AI can be used to overcome three barriers to learning in the classroom: improving transfer, breaking the illusion of explanatory depth, and training students to critically evaluate explanations." (Mollick & Mollick, 2022).

In line with the Mollicks, Lucinda McKnight, senior lecturer in pedagogy and curriculum at Deakin University, offers several suggestions for incorporating genAI into student assignments to support deeper and more robust learning, including:

Use AI writers as researchers. They can research a topic exhaustively in seconds and compile text for review, along with (real & hallucinated) references for students to follow up. This material can then inform original and carefully referenced student writing.

Use AI writers to produce text on a given topic for critique. Design assessment tasks that involve this efficient use of AI writers, then [ask students to provide] critical annotation of the text that is produced.

Use different AI writers to produce different versions of text on the same topic to compare and evaluate.

Use and attribute AI writers for routine text, for example, blog content. Use discrimination to work out where and why AI text, human text, or hybrid text are appropriate and give accounts of this thinking.

Research and establish the specific affordances of AI-based content generators for your discipline. For example, how might it be useful to be able to produce text in multiple languages in seconds? Or create text optimized for search engines?

Explore different ways AI writers and their input can be acknowledged and attributed ethically and appropriately in your discipline. Model effective note-making and record-keeping. Use formative assessment that explicitly involves discussion of the role of AI in given tasks. Discuss how AI could lead to various forms of plagiarism and how to avoid this. (McKnight, 2022).

Whether or not you explicitly incorporate generative AI into all your assignments (or a subset of your assignments), make sure to stress that it doesn't always produce correct answers and provide examples. Underscore that genAI output requires reflection and input from humans.

Many of the resources included at the end of this section provide examples of redesigned assignments from a wide range of disciplines that leverage or mitigate the use of GAI.
Staff from the Teaching + Learning Lab are available to help you revise your assignments and assessments.
Resources

Expert v. novice learners

Hardiman, P.T., Dufresne, R. & Mestre, J.P. (1989). The relation between problem categorization and problem solving among experts and novices. Memory & Cognition 17, 627-638. https://doi.org/10.3758/BF03197085

Larkin, J., McDermott, J., Simon, D.P., & Simon, H. (1980). Expert and Novice Performance in Solving Physics Problems. Science, 208(4450). pp. 1335-1342. DOI:10.1126/science.208.4450.1335

Polya, G., & Conway, J. H. (2014). How to solve it: A New aspect of mathematical method. Princeton University Press.

Wankat, P.C., and F.S. Oreovicz (2015). Problem solving & creativity in Teaching Engineering, Second Edition (pp. 93-115). Purdue University Press. (Open Access Edition)

Assignments

For information on developing psets that incorporate GenAI, see our post: Rethinking Your Problem Sets in the World of Generative AI
For additional ideas for rethinking assignments, see Appendices B-F of Cornell's Committee Report: Generative Artificial Intelligence for Education and Pedagogy
Course Format

If you are committed to limiting students' use of genAI in your subject, you may want consider how a change in its format and/or structure of might impact students' use of genAI. Take a fresh look at Blended Learning (BL). In BL, students generally view recordings of lectures and/or engage with pre-class readings to gain a basic understanding of relevant topics (this is the "information-delivery" component of the class). They then engage in active problem solving (information retrieval and application and knowledge creation) during class time. Depending on the subject (topic, level, etc.), you may be able to restrict students' use of computers during class to ensure that they are engaging in traditional problem solving as they grapple with key problems from the week's material. Additionally, even if students are required to use particular software or applications - you can monitor their use during class time.

Teaching + Learning Lab staff are available to help you implement blended learning in your courses. Additional Resources are provided below.

Equity

Currently, OpenAI's ChatGPT-3.5 is open-access and free but more prone to producing incorrect ("hallucinated") responses. ChatGPT-4 is much "better" than ChatGPT-3.5 but costs $20/month. Bing, Microsoft's version, has comparable capability to ChatGPT-4 and is "free" but requires a Microsoft account and must be used in the Microsoft Edge browser.
Without some planning on your part, some students in your class will have access to ChatGPT-4, while others may only have ChatGPT-3.5 or Bing. See the Assessments & Assignments section for ideas for more equitable incorporation of GenAI in your subjects.

Accessibility

Concerning accessibility, writing in Wired, Pia Ceres writes, "completely barring ChatGPT from classrooms, tempting as that may be, could introduce a host of new problems. Torrey Trust at the University of Massachusetts Amherst studies how teachers use technology to reshape learning. She points out that reverting to analog forms of assessment, like oral exams, can put students with disabilities at a disadvantage." (Ceres, 2023)

See the Transparency in Learning and Teaching project for additional resources for developing transparent and equitable assignments and assessments or contact MIT's Disability and Access Services (DAS).

Student Privacy

If you would like students to engage with AI-generated content in your subjects, consider student privacy issues (e.g., ChatGPT is an open-access tool, not supported by IS&T and not subject to MIT's student data safeguards), as well as the ethics of mandating that students use the tool. Read and encourage all students to read ChatGPT's privacy policy, which states that data collected by ChatGPT can be shared with third-party vendors, law enforcement, affiliates, and other users, and the terms of use, which states that "you must be 18 years or older and able to form a binding contract with OpenAI to use the Services" (i.e., students under 18 years old should not be asked to use the tool.) Users can request to delete their ChatGPT account, but all prompts and inputs to the site cannot be removed.
Writing in her blog, Jill Walker Rettberg, professor of digital culture at the University of Bergen in Norway, notes, "OpenAI knows my email and the country I am connecting from, so they can assume my judgements [sic] about how ChatGPT responds to me align with "Norwegian values." OpenAI also knows what device, browser and operating system I am using, which can be a proxy for class and socio-economic status." (Rettberg, 2022)

To address data privacy concerns, consider ways that students can use AI-generated content without generating it themselves (E.g., you or a TA volunteer could enter questions/prompts as specified by students and share them for use in the assignment).

For additional guidance re student privacy and GAI use, see MIT Sloan's Generative AI for Teaching & Learning Resources Hub

Teaching + Learning Lab Staff are available to help you address these concerns.

MIT Resources and Support

Staff from TLL and Open Learning-Residential are available to facilitate discussions within MIT DLCs on the topic of Teaching with GenAI. MIT community members can email ai-teaching@mit.edu to arrange a session. If you'd like to facilitate your own discussion - a customizable slide deck is available here. [You will be prompted to make a copy.]
Sloan - Teaching & Learning with Generative AI Hub
GAI Writing Guide for Students, Prof. Mary Fuller (Literature & current Chair of the MIT Faculty)

Advice Concerning the Increase in AI-Assisted Writing, Profs. Ed Schiappa and Nicholas Montfort, Comparative Media Studies & Writing @ MIT.

If you are an MIT community member and have resources you'd like to share, please email us at: ai-teaching@mit.edu.

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Over the years, our ideas have changed regarding what students need to know how to do "from scratch" and what they can outsource to technology. For example, many courses permit (encourage) the use of software packages for mathematical operations and/or the analysis of scientific and engineering processes (e.g., Wolfram Alpha, Matlab, etc.)

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Harvard University: Designing Courses and Assignments in the Age of AI

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Harvard University: Designing Courses and Assignments in the Age of AI

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Provides guidance for integrating AI into both the process and product stages of learning. Examples include workflow logs, source-based critique, revision rationales, model comparison, brainstorming followed by evaluation, and short demonstrations that confirm student mastery.

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Designing Courses & Assignments in the Age of AI | The Derek Bok Center for Teaching and Learning

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The vast majority of undergraduate students at Harvard College are using generative AI. Many are using it for help on their academic work, often regardless of stated course policies. A 2024 survey of Harvard undergraduates found that 85% use AI in some way at least biweekly, and over 50% rely on it specifically for writing assignments. National data from 2025 shows even higher rates. These tools are not just pervasive; their capabilities can lead to a range of issues for instructors as they give feedback on student work and, by extension, assess student learning.

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Cornell University: AI in Assignment Design

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Cornell University: AI in Assignment Design

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Emphasizes that AI can support creation and revision but may also bypass the process through which learning occurs. It guides faculty in identifying essential knowledge, determining what students must practice, scaffolding learning activities, and using AI to support application and critical thinking rather than replace them.

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EDUCAUSE: Advancing AI Literacy Through a Faculty Course Refresh Institute

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EDUCAUSE: Advancing AI Literacy Through a Faculty Course Refresh Institute

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Describes a structured faculty course-redesign process that moves beyond simply adding an AI tool. Faculty reconsider learning experiences, develop AI-literacy outcomes, redesign activities and assessments, and decide when students should work independently and when they should work with AI.

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Advancing AI Literacy: A Faculty Course Refresh Institute at Indiana Wesleyan University

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Redesigning learning experiences, not just adding tools, can build faculty confidence and develop students’ critical, ethical, and applied AI literacy

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University of Illinois Chicago: Adapting Assignments for the Age of AI

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University of Illinois Chicago: Adapting Assignments for the Age of AI

# data[].attributes.body
Begins redesign with course goals and learning objectives rather than concerns about cheating. It addresses transparent expectations, assignment purpose, process documentation, reflection, formative activities, authentic applications, and the need to explain how AI use relates to learning.

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Provides practical strategies for deciding whether an assignment should integrate, limit, or critically examine AI. It emphasizes authentic disciplinary work, original thinking, reflection, contextualized tasks, and clear explanations of why particular forms of AI use are or are not appropriate.

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Offers a self-check for reviewing courses and assessments in light of AI. It asks what knowledge students should retain, which skills they must master with and without AI, which learning modalities matter, and how instructors can keep learning purposeful and meaningful.

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AI Health Check | Teaching Support and Innovation

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Keli Yerian, Distinguished Teaching Professor in Linguistics, launched an AI Health Check for faculty teaching

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University of Oregon: Assessing Learning as a Process

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University of Oregon: Assessing Learning as a Process

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Focuses on designing courses that capture evidence of thinking, revision, decision-making, feedback, and growth rather than relying only on polished final products. It provides a useful foundation for staged assignments, formative checkpoints, reflection, and visible learning processes.

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Assessing Learning as a Process | Teaching Support and Innovation

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Reflection activities help students deepen learning, build critical thinking, and connect course content to real-world applications. They also encourage authentic, multi-tiered, process-focused work. In this section, you’ll find a range of strategies from individual reflections to group-based activities that can be adapted across disciplines to support student growth.

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University of Oregon: Authentic Assessment and Generative AI

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University of Oregon: Authentic Assessment and Generative AI

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Connects course learning with professional, cultural, civic, and real-world contexts that give students a reason to engage. It emphasizes open-ended problems, student decision-making, multiple possible solutions, meaningful application, process awareness, and demonstrations of mastery.

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Authentic Assessment | Teaching Support and Innovation

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This page explores authentic assessment as an strategy that links course content and activities to political, cultural, and professional contexts that motivate students to learn. To inspire intrinsic motivation, authentic assessments shift student “focus towards real-world problem solving” and encourage students “to apply knowledge” in the scenarios they care about most (Conrad). Authentic assessment does not focus on the “right” answer; it asks students to develop an awareness of the process and decision-making they use to reach their conclusions. This page covers strategies, tips, examples, and resources for implementing authentic assessments in your course.

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Columbia University: Teaching Nursing with AI

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Columbia University: Teaching Nursing with AI

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Provides a concrete example of a full course redesign rather than a single AI activity. The redesigned course added AI-literacy outcomes, introduced foundational AI concepts, integrated AI into case studies, and used ChatGPT as a scaffold that helped novice students progress toward more advanced cognitive work.

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Faculty across Columbia University are experimenting with AI in their courses and reimagining their course policies, assignments, and activities to support student learning while transparently communicating expectations to their students about the use of generative AI. Here, Meghan Reading Turchioe shares her approach to experimenting with AI in the classroom.

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MIT Teaching + Learning Lab: AI-Aware Implementation and Assessment Examples

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MIT Teaching + Learning Lab: AI-Aware Implementation and Assessment Examples

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Provides examples of assignments redesigned to promote higher-order cognitive skills through purposeful AI use, as well as examples that limit AI when independent mastery is the intended outcome. It helps faculty connect the role of AI to the specific learning outcome being assessed.

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AI-Aware Implementation: Assessment Examples | Teaching + Learning Lab

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Exploring the tension between accuracy & overall meaning in translation

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Collects faculty-designed examples of AI-responsive learning experiences across disciplines. The activities emphasize experimentation, creation, critique, reflection, student explanation, and examination of the choices made while working with AI.

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AI in Teaching & Learning | Center for Teaching & Learning

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This resource is designed to support Berkeley instructors as they navigate the evolving role of generative AI in teaching and learning. It offers ideas, examples, and tools to help you make informed decisions about GenAI use in your course.

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Stanford Graduate School of Education: Beyond ChatGPT, Critical Thinking in the Age of AI

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Stanford Graduate School of Education: Beyond ChatGPT, Critical Thinking in the Age of AI

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Examines how AI can supplement rather than supplant the learning process. It emphasizes balancing student intellectual contribution with AI output and designing learning experiences that preserve critical thinking, creativity, productive struggle, and active engagement.

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Beyond ChatGPT: Critical thinking in the age of AI

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In this episode of School’s In, GSE Assistant Professor Hari Subramonyam discusses how AI can support critical thinking and creativity in the learning process.

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Lamar University: Promoting Authenticity of Student Work in the Age of Artificial Intelligence

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Lamar University: Promoting Authenticity of Student Work in the Age of Artificial Intelligence

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Provides a comprehensive faculty guide addressing AI integration, academic integrity, authentic assessment, and meaningful evidence of student learning. It includes more than 100 assignment examples designed to promote human input, critical analysis, creativity, communication, and disciplinary application.

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The University of Texas at Austin: Generative AI in Teaching and Learning, Course Design

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The University of Texas at Austin: Generative AI in Teaching and Learning, Course Design

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Encourages instructors to use research-driven course design and consider how AI may augment learning, research-skill development, student agency, and shared responsibility for learning. The resource connects AI decisions with outcomes, instructional activities, assessments, and effective teaching principles.

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Generative AI in Teaching and Learning: Course Design

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Texas Tech University: AI Teaching Resources

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Texas Tech University: AI Teaching Resources

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Provides guidance on appropriate uses of AI in teaching, including course redesign, sequencing course content, developing active-learning strategies, creating assessments, and helping students critically evaluate AI-generated information.

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AI Teaching Resources | TLPDC Home | Texas Tech

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This page hosts information and teaching resources regarding artificial intelligence for faculty at Texas Tech University.

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University of Houston: Teaching Innovation Program AI Awardees

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University of Houston: Teaching Innovation Program AI Awardees

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Offers numerous examples of AI-supported course and curriculum redesign across humanities, engineering, business, health, communication, architecture, sociology, languages, and other disciplines. Projects emphasize critical thinking, creativity, real-world applications, student engagement, and purposeful integration of AI into learning.

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From Theory to Practice: Enhancing Thermal Design Education with AI and Real-life Thermodynamic DevicesBo ZhaoMechanical and Aerospace Engineering

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Southern Methodist University: Course Design with Generative AI

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Southern Methodist University: Course Design with Generative AI

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Describes an immersive course-design process for faculty building a new course or substantially rethinking an existing one. The initiative positions generative AI as part of intentional course development rather than as an isolated classroom tool.

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Faculty who use AI in the classroom should provide clear guidelines around AI use in the class for graded and ungraded assignments, self-study outside of class, and support through other SMU avenues, as well as a disclosure statement if AI is used for other pedagogical aspects such as assignment building or grading.

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U.S. Department of Education: Guidance on the Use of AI in Schools

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U.S. Department of Education: Guidance on the Use of AI in Schools

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Provides current federal guidance on responsible AI integration in education. It addresses instructional materials, tutoring, college and career advising, student privacy, stakeholder engagement, and allowable uses of federal grant funding.

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U.S. Department of Education: Artificial Intelligence and the Future of Teaching and Learning

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U.S. Department of Education: Artificial Intelligence and the Future of Teaching and Learning

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A foundational federal report focused primarily on K-12 education. It examines AI-supported learning, teaching, assessment, research, policy, human oversight, algorithmic bias, and the need to place educators and students at the center of technology decisions.

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TeachAI: AI Guidance for Schools Toolkit

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TeachAI: AI Guidance for Schools Toolkit

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Helps school systems develop responsible AI guidance and policies. It includes guiding principles, sample language, real-world examples, stakeholder resources, and materials for educators, students, families, and district leaders.

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TeachAI | AI Guidance for Schools Toolkit

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The AI Guidance for Schools Toolkit is designed to help education authorities, school leaders and teachers create their own thoughtful guidance to achieve potential benefits of incorporating artificial intelligence in primary and secondary K-12 education while mitigating risks.

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CoSN and Council of the Great City Schools: K-12 Generative AI Maturity Tool

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CoSN and Council of the Great City Schools: K-12 Generative AI Maturity Tool

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Helps districts evaluate their readiness to implement generative AI across leadership, instruction, operations, data, technology, security, legal compliance, and academic AI literacy. It can support district planning and roadmap development.

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CoSN/CGCS K-12 Gen AI Maturity Tool | CoSN

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This Generative Artificial Intelligence (Gen AI) Maturity Tool is intended to serve as a starting point for school districts to intelligently and thoughtfully implement AI technologies that align with instructional and operational objectives. This tool builds on The AI Readiness Checklist as described below. We hope you find it useful…

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ISTE+ASCD: Artificial Intelligence in Education

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ISTE+ASCD: Artificial Intelligence in Education

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Provides K-12 educators with AI resources, professional learning, educator communities, classroom ideas, and materials focused on safe and responsible teaching and learning with AI.

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ISTE offers free resources and guides for educators who are interested in learning about how artificial intelligence can empower learners at every level.

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Computer Science Teachers Association: AI Learning Priorities for All K-12 Students

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Computer Science Teachers Association: AI Learning Priorities for All K-12 Students

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Provides grade-band guidance on the AI concepts, knowledge, and skills students should develop. The priorities can help standards writers, curriculum developers, districts, and teachers build coherent K-12 AI-learning experiences.

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AI Learning Priorities for All K-12 Students - Computer Science Teachers Association

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AI Learning Prioritiesfor All K-12 Students AI Learning Prioritiesfor All K-12 Students Vision All students must learn about artificial intelligence…

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Offers free, open-source curriculum, assessments, educator guides, student materials, and professional development for teaching AI across K-12 grade levels. Lessons address how AI works, its applications, its risks, and responsible design and use.

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Learn the Basics

What is artificial intelligence (AI)?

What is machine learning and how does it work?

How will AI shape my life and that of my community?

From social media, entertainment, agriculture, travel, space exploration, climate forecasting, marine biology, sports analytics, healthcare diagnostics, to the future of work overall, AI has and will continue to have profound impact on our lives.

AI is for everyone. Empower your students with the knowledge and skills to understand how they use and create with these technologies responsibly and ethically.

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What is artificial intelligence (AI)? What is machine learning and how does it work? How will AI shape my life and that of my community? From social media, entertainment, agriculture, travel, space exploration, climate forecasting, marine biology, sports analytics, healthcare diagnostics, to the future of work overall, AI has and will continue to have profound impact on our lives.

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Defines AI literacy around understanding, evaluating, and using AI safely and effectively. The framework includes core values, modes of engagement, types of AI use, and practical strategies for incorporating AI literacy throughout K-12 education.

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As artificial intelligence (AI) becomes increasingly prevalent in our daily lives and educational settings, it is imperative to equip learners, educators, and leaders with the necessary skills and knowledge to navigate this emerging technology responsibly. Our AI Literacy Framework (depicted below) consists of three interconnected Modes of Engagement: Understand, Evaluate, and Use. The framework emphasizes that understanding and evaluating AI are critical to making informed decisions about if and how to use AI in learning environments.

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Stanford Graduate School of Education: Classroom-Ready Resources About AI for Teaching

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Stanford Graduate School of Education: Classroom-Ready Resources About AI for Teaching

# data[].attributes.body
Provides free, adaptable AI-literacy lessons for high school classrooms. The multidisciplinary resources help students explore how AI works, question its outputs, examine its social implications, and critically evaluate AI systems.

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CRAFT is empowering students with AI Literacy.

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Free resources to help you teach high school students in all subject areas to understand and question Artificial Intelligence

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EDSAFE AI Alliance: SAFE Benchmarks Framework

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EDSAFE AI Alliance: SAFE Benchmarks Framework

# data[].attributes.body
Provides a values-based framework for evaluating and governing AI in education. It emphasizes safety, accountability, fairness, transparency, human oversight, student data protection, accessibility, and institutional responsibility.

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Overview of the EDSAFE AI SAFE framework for educators, policymakers, and the education community. Focused on safe, accountable, fair transparent, and efficacious AI use in education

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Lamar University: Guidelines on AI Detection Software

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Lamar University: Guidelines on AI Detection Software

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States that Lamar University does not support using AI detection software to evaluate suspected student AI use because of concerns about false positives, inaccurate conclusions, and unfair outcomes. It advises faculty not to use unapproved detection tools and instead recommends authentic assessment strategies, formative assessment, peer review, interactive assessments, detailed rubrics, supported proctoring tools, and consultation with the Center for Innovation in Teaching and Learning.

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Weber-Wulff et al.: Testing of Detection Tools for AI-Generated Text

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Weber-Wulff et al.: Testing of Detection Tools for AI-Generated Text

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This peer-reviewed study evaluated 14 detection systems, including commercial tools used in education. The researchers concluded that the available tools were neither sufficiently accurate nor reliable and that paraphrasing, translation, and text modification further reduced their effectiveness.

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Testing of detection tools for AI-generated text

Abstract

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

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Article Open access

Introduction

Higher education institutions (HEIs) play a fundamental role in society. They shape the next generation of professionals through education and skill development, simultaneously providing hubs for research, innovation, collaboration with business, and civic engagement. It is also in higher education that students form and further develop their personal and professional ethics and values. Hence, it is crucial to uphold the integrity of the assessments and diplomas provided in tertiary education.

The introduction of unauthorised content generation-"the production of academic work, in whole or part, for academic credit, progression or award, whether or not a payment or other favour is involved, using unapproved or undeclared human or technological assistance" (Foltýnek et al. 2023)-into higher education contexts poses potential threats to academic integrity. Academic integrity is understood as "compliance with ethical and professional principles, standards and practices by individuals or institutions in education, research and scholarship" (Tauginienė et al. 2018).

Recent advancements in artificial intelligence (AI), particularly in the area of the generative pre-trained transformer (GPT) large language models (LLM), have led to a range of publicly available online text generation tools. As these models are trained on human-written texts, the content generated by these tools can be quite difficult to distinguish from human-written content. They can thus be used to complete assessment tasks at HEIs.

Despite the fact that unauthorised content generation created by humans, such as contract cheating (Clarke & Lancaster 2006), has been a well-researched form of student cheating for almost two decades now, HEIs were not prepared for such radical improvements in automated tools that make unauthorised content generation so easily accessible for students and researchers. The availability of tools based on GPT-3 and newer LLMs, ChatGPT (OpenAI 2023a, b) in particular, as well as other types of AI-based tools such as machine translation tools or image generators, have raised many concerns about how to make sure that no academic performance deception attempts have been made. The availability of ChatGPT has forced HEIs into action.

Unlike contract cheating, the use of AI tools is not automatically unethical. On the contrary, as AI will permeate society and most professions in the near future, there is a need to discuss with students the benefits and limitations of AI tools, provide them with opportunities to expand their knowledge of such tools, and teach them how to use AI ethically and transparently.

Nonetheless, some educational institutions have directly prohibited the use of ChatGPT (Johnson 2023), and others have even blocked access from their university networks (Elsen-Rooney 2023), although this is just a symbolic measure with virtual private networks quite prevalent. Some conferences have explicitly prohibited AI-generated content in conference submissions, including machine-learning conferences (ICML 2023). More recently, Italy became the first country in the world to ban the use of ChatGPT, although that decision has in the meantime been rescinded (Schechner 2023). Restricting the use of AI-generated content has naturally led to the desire for simple detection tools. Many free online tools that claim to be able to detect AI-generated text are already available.

Some companies do urge caution when using their tools for detecting AI-generated text for taking punitive measures based solely on the results they provide. They acknowledge the limitations of their tools, e.g. OpenAI explains that there are several ways to deceive the tool (OpenAI 2023a, b, 8 May). Turnitin made a guide for teachers on how they should approach the students whose work was flagged as AI-generated (Turnitin 2023a, b, 16 March). Nevertheless, four different companies (GoWinston, 2023; Content at Scale 2023; Compilatio 2023; GPTZero 2023) claim to be the best on the market.

The aim of this paper is to examine the general functionality of tools for the detection of the use of ChatGPT in text production, assess the accuracy of the output provided by these tools, and their efficacy in the face of the use of obfuscation techniques such as online paraphrasing tools, as well as the influence of machine translation tools to human-written text.

Specifically, the paper aims to answer the following research questions:

RQ1: Can detection tools for AI-generated text reliably detect human-written text?

RQ2: Can detection tools for AI-generated text reliably detect ChatGPT-generated text?

RQ3: Does machine translation affect the detection of human-written text?

RQ4: Does manual editing or machine paraphrasing affect the detection of ChatGPT-generated text?

RQ5: How consistent are the results obtained by different detection tools for AI-generated text?

The next section briefly describes the concept and history of LLMs. It is followed by a review of scientific and non-scientific related work and a detailed description of the research methodology. After that, the results are presented in terms of accuracy, error analysis, and usability issues. The paper ends with discussion points and conclusions made.still gained 1.0 points as in the previous methods. The formula for accuracy calculation

Large language models

We understand LLMs as systems trained to predict the likelihood of a specific character, word, or string (called a token) in a particular context (Bender et al. 2021). Such statistical language models have been used since the 1980s (Rosenfeld 2000), amongst other things for machine translation and automatic speech recognition. Efficient methods for the estimation of word representations in multidimensional vector spaces (Mikolov et al. 2013), together with the attention mechanism and transformer architecture (Vaswani et al. 2017) made generating human-like text not only possible, but also computationally feasible.

ChatGPT is a Natural Language Processing system that is owned and developed by OpenAI, a research and development company established in 2015. Based on the transformer architecture, OpenAI released the first version of GPT in June 2018. Within less than a year, this version was replaced by a much improved GPT-2, and then in 2020 by GPT-3 (Marr 2023). This version could generate coherent text within a given context. This was in many ways a game-changer, as it is capable of creating responses that are hard to distinguish from human-written text (Borji 2023; Brown et al. 2020). As 7% of the training data is on languages other than English, GPT-3 can also perform multilingually (Brown et al. 2020). In November 2022, ChatGPT was launched. It demonstrated significant improvements in its capabilities, a user-friendly interface, and it was widely reported in the general press. Within two months of its launch, it had over 100 million subscribers and was labelled "the fastest growing consumer app ever" (Milmo 2023).

AI in education brings both challenges and opportunities. Authorised and properly acknowledged usage of AI tools, including LLMs, is not per se a form of misconduct (Foltýnek et al. 2023). However, using AI tools in an educational context for unauthorised content generation (Foltýnek et al. 2023) is a form of academic misconduct (Tauginienė et al. 2018). Although LLMs have become known to the wider public after the release of ChatGPT, there is no reason to assume that they have not been used to create unauthorised and undeclared content even before that date. The accessibility, quantity, and recent development of AI tools have led many educators to demand technical solutions to help them distinguish between human-written and AI-generated texts.

For more than two decades, educators have been using software tools in an attempt to detect academic misconduct. This includes using search engines and text-matching software in order to detect instances of potential plagiarism. Although such automated detection can identify some plagiarism, previous research by Foltýnek et al. ( 2020) has shown that text-matching software not only do not find all plagiarism, but furthermore will also mark non-plagiarised content as plagiarism, thus providing false positive results. This is a worst-case scenario in academic settings, as an honest student can be accused of misconduct. In order to avoid such a scenario, now, when the market has responded with the introduction of dozens of tools for AI-generated text, it is important to discuss whether these tools clearly distinguish between human-written and machine-generated content.

Related work

The development of LLMs has led to an acceleration of different types of efforts in the field of automatic detection of AI-generated text. Firstly, several researchers has studied human abilities to detect machine-generated texts (e.g. Guo et al. 2023; Ippolito et al. 2020; Ma et al. 2023). Secondly, some attempts have been made to build benchmark text corpora to detect AI-generated texts effectively; for example, Liyanage et al. ( 2022) have offered synthetic and partial text substitution datasets for the academic domain. Thirdly, many research works are focused on developing new or fine-tuning parameters of the already pre-trained models of machine-generated text (e.g. Chakraborty et al. 2023; Devlin et al. 2019).

These efforts provide a valuable contribution to improving the performance and capabilities of detection tools for AI-generated text. In this section, the authors of the paper mainly focus on studies that compare or test the existing detection tools that educators can use to check the originality of students' assignments. The related works examined in the paper are summarised in Tables 1, 2, and 3. They are categorised as published scientific publications, preprints and other publications. It is worth mentioning that although there are many comparisons on the Internet made by individuals and organisations, Table 3 includes only those with the higher coverage of tools and/or at least partly described methodology of experiments.

Some researchers have used known text-matching software to check if they are able to find instances of plagiarism in the AI-generated text. Aydin and Karaarslan ( 2022) tested the iThenticate system and have revealed that the tool has found matches with other information sources both for ChatGPT-paraphrased text and -generated text. They also found that ChatGPT does not produce original texts after paraphrasing, as the match rates for paraphrased texts were very high in comparison to human-written and ChatGPT-generated text passages. In the experiment of Gao et al. ( 2022), Plagiarismdetector.net recognized nearly all of the fifty scientific abstracts generated by ChatGPT as completely original.

Khalil and Er (Khalil and Er 2023) fed 50 ChatGPT-generated essays into two text-matching software systems (25 essays to iThenticate and 25 essays to the Turnitin system), although they are just different interfaces to the same engine. They found that 40 (80%) of them were considered to have a high level of originality, although they defined this as a similarity score of 20% or less. Khalil and Er (Khalil and Er 2023) also attempted to test the capabilities of ChatGPT to detect if the essays were generated by ChatGPT and state an accuracy of 92%, as 46 essays were supposedly said to be cases of plagiarism. As of May 2023, ChatGPT now issues a warning to such questions such as: "As an AI language model, I cannot verify the specific source or origin of the paragraph you provided."

The authors of this paper consider the study of Khalil and Er (Khalil and Er 2023) to be problematic for two reasons. First, it is worth noting that the application of text-matching software systems to the detection of LLM-generated text makes little sense because of the stochastic nature of the word selection. Second, since an LLM will "hallucinate", that is, make up results, it cannot be asked whether it is the author of a text.

Several researchers focused on testing sets of free and/or paid detection tools for AI-generated text. Wang et al. ( 2023) checked the performance of detection tools on both natural language content and programming code and determined that "detecting ChatGPT-generated code is even more difficult than detecting natural language contents." They also state that tools often exhibit bias, as some of them have a tendency to predict that content is ChatGPT generated (positive results), while others tend to predict that it is human-written (negative results).

By testing fifty ChatGPT-generated paper abstracts on the GPT-2 Output detector, Gao et al. ( 2022) concluded that the detector was able to make an excellent distinction between original and generated abstracts because the majority of the original abstracts were scored extremely low (corresponding to human-written content) while the detector found a high probability of AI-generated text in the majority (33 abstracts) of the ChatGPT-generated abstracts with 17 abstracts scored below 50%.

Pegoraro et al. ( 2023) tested not only online detection tools for AI-generated text but also many of the existing detection approaches and claimed that detection of the ChatGPT-generated text passages is still a very challenging task as the most effective online detection tool can only achieve a success rate of less than 50%. They also concluded that most of the analysed tools tend to classify any text as human-written.

Tests completed by van Oijen ( 2023) showed that the overall accuracy of tools in detecting AI-generated text reached only 27.9%, and the best tool achieved a maximum of 50% accuracy, while the tools reached an accuracy of almost 83% in detecting human-written content. The author concluded that detection tools for AI-generated text are "no better than random classifiers" (van Oijen 2023). Moreover, the tests provided some interesting findings; for example, the tools found it challenging to detect a piece of human-written text that was rewritten by ChatGPT or a text passage that was written in a specific style. Additionally, there was not a single attribution of a human-written text to AI-generated text, that is, an absence of false positives.

Although Demers ( 2023) only provided results of testing without any further analysis, their examination allows making conclusions that a text passage written by a human was recognised as human-written by all tools, while ChatGPT-generated text had a mixed evaluation with the tendency to be predicted as human-written (10 tools out of 16) that increased even further for the ChatGPT writing sample with the additional prompt "beat detection" (12 tools out of 16).

Elkhatat et al.( 2023) revealed that detection tools were generally more successful in identifying GPT-3.5-generated text than GPT-4-generated text and demonstrated inconsistencies (false positives and uncertain classifications) in detecting human-written text. They also questioned the reliability of detection tools, especially in the context of investigating academic integrity breaches in academic settings.

In the tests conducted by Compilatio, the detection tools for AI-generated text detected human-written text with reliability in the range of 78-98% and AI-generated text - 56-88%. Gewirtz' ( 2023) results on testing three human-written and three ChatGPT-generated texts demonstrated that two of the selected detection tools for AI-generated text could reach only 50% accuracy and one an accuracy of 66%.

The effect of paraphrasing on the performance of detection tools for AI-generated text has also been studied. For example, Anderson et al. ( 2023) concluded that paraphrasing has significantly lowered the detection capabilities of the GPT-2 Output Detector by increasing the score for human-written content from 0.02% to 99.52% for the first essay and from 61.96% to 99.98% for the second essay. Krishna et al. ( 2023) applied paraphrasing to the AI-generated texts and revealed that it significantly lowered the detection accuracy of five detection tools for AI-generated text used in the experiments.

The results of the above-mentioned studies suggest that detecting AI-generated text passages is still challenging for existent detection tools for AI-generated text, whereas human-written texts are usually identified quite accurately (accuracy above 80%). However, the ability of tools to identify AI-generated text is under question as their accuracy in many studies was only around 50% or slightly above. Depending on the tool, a bias may be observed identifying a piece of text as either ChatGPT-generated or human-written. In addition, tools have difficulty identifying the source of the text if ChatGPT transforms human-written text or generates text in a particular style (e.g. a child's explanation). Furthermore, the performance of detection tools significantly decreases when texts are deliberately modified by paraphrasing or re-writing. Detection of the AI-generated text remains challenging for existing detection tools, but detecting ChatGPT-generated code is even more difficult.

Existing research has several shortcomings:

quite often experiments are carried out with a limited number of detection tools for AI-generated text on a limited set of data;

sometimes human-written texts are taken from publicly available websites or recognised print sources, and thus could potentially have been previously used to train LLMs and/or provide no guarantee that they were actually written by humans;

the methodological aspects of the research are not always described in detail and are thus not available for replication;

testing whether the AI-generated and further translated text can influence the accuracy of the detection tools is not discussed at all;

a limited number of measurable metrics is used to evaluate the performance of detection tools, ignoring the qualitative analysis of results, for example, types of classification errors that can have significant consequences in an academic setting.

Methodology

Test cases

The focus of this research is determining the accuracy of tools which state that they are able to detect AI-generated text. In order to do so, a number of situational parameters were set up for creating the test cases for the following categories of English-language documents:

human-written;

human-written in a non-English language with a subsequent AI/machine translation to English;

AI-generated text;

AI-generated text with subsequent human manual edits;

AI-generated text with subsequent AI/machine paraphrase.

For the first category (called 01-Hum), the specification was made that 10.000 characters (including spaces) were to be written at about the level of an undergraduate in the field of the researcher writing the paper. These fields include academic integrity, civil engineering, computer science, economics, history, linguistics, and literature. None of the text may have been exposed to the Internet at any time or even sent as an attachment to an email. This is crucial because any material that is on the Internet is potentially included in the training data for an LLM.

For the second category (called 02-MT), around 10.000 characters (including spaces) were written in Bosnian, Czech, German, Latvian, Slovak, Spanish, and Swedish. None of this texts may have been exposed to the Internet before, as for 01-Hum. Depending on the language, either the AI translation tool DeepL (3 cases) or Google Translate (6 cases) was used to produce the test documents in English.

It was decided to use ChatGPT as the only AI-text generator for this investigation, as it was the one with the largest media attention at the beginning of the research. Each researcher generated two documents with the tool using different prompts, (03-AI and 04-AI) with a minimum of 2000 characters each and recorded the prompts. The language model from February 13, 2023 was used for all test cases.

Two additional texts of at least 2000 characters were generated using fresh prompts for ChatGPT, then the output was manipulated. It was decided to use this type of test case, as students will have a tendency to obfuscate results with the expressed purpose of hiding their use of an AI-content generator. One set (05-ManEd) was edited manually with a human exchanging some words with synonyms or reordering sentence parts and the other (06-Para) was rewritten automatically with the AI-based tool Quillbot (Quillbot 2023), using the default values of the tool for modes (Standard) and synonym level. Documentation of the obfuscation, highlighting the differences between the texts, can be found in the Appendix.

With nine researchers preparing texts (the eight authors and one collaborator), 54 test cases were thus available for which the ground truth is known.

AI-generated text detection tool selection

A list of detection tools for AI-generated text was prepared using social media and Google search. Overall, 18 tools were considered, out of which 6 were excluded: 2 were not available, 2 were not online applications but Chrome extensions and thus out of the scope of this research, 1 required payment, and 1 did not produce any quantifiable result.

The company Turnitin approached the research group and offered a login, noting that they could only offer access from early April 2023. It was decided to test the system, although it is not free, because it is so widely used and already widely discussed in academia. Another company, PlagiarismCheck, was also advertising that it had a detection tool for AI-generated text in addition to its text-matching detection system. It was decided to ask them if they wanted to be part of the test as well, as the researchers did not want to have only one paid system. They agreed and provided a login in early May. We caution that their results may be different from the free tools used, as the companies knew that the submitted documents were part of a test suite and they were able to use the entire test document.

The following 14 detection tools were tested:

Table 4 gives an overview of the minimum/maximum sizes of text that could be examined by the free tools at the time of testing, if known.

PlagiarismCheck and Turnitin are combined text similarity detectors and offer an additional functionality of determining the probability the text was written by an AI, so there was no limit on the amount of text tested. Signup was necessary for Check for AI, Crossplag, Go Winston, GPT Zero, and OpenAI Text Classifier (a Google account worked).

Data collection

The tests were run by the individual authors between March 7 and March 28, 2023. Since Turnitin was not available until April, those tests were completed between April 14 and April 20, 2023. The testing of PlagiarismCheck was performed between May 2 and May 8, 2023. All the 54 test cases had been presented to each of the tools for a total of 756 tests.

Evaluation methodology

For the evaluation, the authors were split into groups of two or three and tasked with evaluating the results of the tests for the cases from either 01-Hum & 04-AI, 02-MT & 05-ManEd, or 03-AI & 06-Para. Since the tools do not provide an exact binary classification, one five-step classification was used for the original texts (01-Hum & 02-MT) and another one was used for the AI-generated texts (03-AI, 04-AI, 05-ManEd & 06-Para). They were based on the probabilities that were reported for texts being human-written or AI-generated as specified in Table 5.

For four of the detection tools, the results were only given in the textual form ("very low risk", "likely AI-generated", "very unlikely to be from GPT-2", etc.) and these were mapped to the classification labels as given in Table 6.

After all of the classifications were undertaken and disagreements ironed out, the measures of accuracy, the false positive rate, and the false negative rate were calculated.

Results

Having evaluated the classification outcomes of the tools as (partially) true/false positives/negatives, the researchers evaluated this classification on two criteria: accuracy and error type. In general, classification systems are evaluated using accuracy, precision, and recall. The research authors also conducted an error analysis since the educational context means different types of error have different significance.

Accuracy

When no partial results are allowed, i.e. only TN, TP, FN, and FP are allowed, accuracy is defined as a ratio of correctly classified cases to all cases

$$\mathrm{ACC}=(\mathrm{TN}+\mathrm{TP}) / (\mathrm{TN}+\mathrm{TP}+\mathrm{FN}+\mathrm{FP});$$

As our classificaion contains also partially correct and partially incorrect results (i.e., five classes instead of two), the basic commonly used formula has to be adjusted to properly count these cases. There is no standard way of how this adjustment should be done. Therefore, we will use three different methods which we believe reflect different approaches that educators may have when interpreting tools' outputs. The first (binary) approach is to consider partially correct classification as incorrect and calculate the accuracy as

$$\mathrm{ACC}\_\mathrm{bin}=(\mathrm{TN}+\mathrm{TP}) / (\mathrm{TN}+\mathrm{PTN}+\mathrm{TP}+\mathrm{PTP}+\mathrm{FN}+\mathrm{PFN}+\mathrm{FP}+\mathrm{PFP}+\mathrm{UNC})$$

For the systems providing percentages of confidence, this method basically sets the threshold of 80% (see Table 5). Table 7 shows the number of correctly classified documents, i.e. the sum of true positives and true negatives. The maximum for each cell is 9 (because there were 9 documents in each class), the overall maximum is 9 * 6 = 54. The accuracy is calculated as a ratio of the total and the overall maximum. Note that even the highest accuracy values are below 80%. The last row shows the average accuracy for each document class, across all the tools.

This method provides a good overview of the number of cases in which the classifiers are "sure" about the outcome. However, for real-life educational scenarios, partially correct classifications are also valuable. Especially in case 05-ManEd, which involved human editing, the partially positive classification results make sense. Therefore, the researchers explored more ways of assessment. These methods differ in the score awarded to various incorrect outcomes.

In our second approach, we include partially correct evaluations and count them as correct ones. The formula for accuracy computation is.

$$\mathrm{ACC}\_\mathrm{bin}\_\mathrm{incl}=(\mathrm{TN}+\mathrm{PTN}+\mathrm{TP}+\mathrm{PTP}) / (\mathrm{TN}+\mathrm{PTN}+\mathrm{TP}+\mathrm{PTP}+\mathrm{FN}+\mathrm{PFN}+\mathrm{FP}+\mathrm{PFP}+\mathrm{UNC})$$

In case of systems providing percentages, this method basically sets the threshold of 60% (see Table 5). The results of this classification approach may be found in Table 8. Obivously, all systems achieved higher accuracy, and the systems that provided more partially correct results (GPT Zero, Check for AI) influenced the order.

In our third approach, which we call semi-binary evaluation, the researchers distinguish partially correct classifications (PTN or PTP) both from the correct and incorrect ones. The partially correct classifications were awarded 0.5 points, while entirely correct classification (TN or TP) still gained 1.0 points as in the previous methods. The formula for accuracy calculation is

$$\mathrm{ACC}\_\mathrm{semibin}=(\mathrm{TN}+\mathrm{TP}+0.5 *\mathrm{ PTN}+0.5 *\mathrm{ PTP})\,/ (\mathrm{TN}+\mathrm{PTN}+\mathrm{TP}+\mathrm{PTPFN}+\mathrm{PFN}+\mathrm{FP}+\mathrm{PFP}+\mathrm{UNC})$$

Table 9 shows the assessment results of the classifiers using semi-binary classification. The values correspond to the number of correctly classified documents with partially correct results awarded half a point (TP + TN + 0.5 * PTN + 0.5 * PTP). The maximum value is again 9 for each cell and 54 for the total.

A semi-binary approach to accuracy calculation captures the notion of partially correct classification but still does not distinguish between various forms of incorrect classification. We address this issue by employing a third,-logarithmic approach to accuracy calculation that awards 1 point to completely incorrect classification and doubles the score for each level of the classification that was closer to the correct result. The scores for the particular classifier outputs are shown in Table 10 and the overall scores of the classifiers are shown in Table 11. Note that the maximum value for each cell is now 9 * 16 = 864. The accuracy, again, is calculated as a ratio of the total score and the maximum possible score. This approach provides the most detailed distinction among all varieties of (in)correctness.

As can be seen from Tables 7, 8, 9, and 11, the approach to accuracy evaluation has almost no influence on the ranking of the classifiers. Figure 1 presents the overall accuracy for each tool as the mean of all accuracy approaches used.

Turnitin received the highest score using all approaches to accuracy classification, followed by Compilatio and GPT-2 Output Detector (again in all approaches). This is particularly interesting because as the name suggests, GPT-2 Output Detector was not trained to detect GPT-3.5 output. Crossplag and Go Winston were the only other tools to achieve at least 70% accuracy.

Variations in accuracy

As Fig. 2 above shows, the overall average accuracy figure is misleading, as it obscures major variations in accuracy between document types. Further analysis reveals the influence of machine translation, human editing, and machine paraphrasing on overall accuracy:

Influence of machine translation

The overall accuracy for case 01-Hum (human-written) was 96%. However, in the case of the documents written by humans in languages other than English that were machine-translated to English (case 02-MT), the accuracy dropped by 20%. Apparently, machine translation leaves some traces of AI in the output, even if the original was purely human-written.

Influence of human manual editing

Case 05-ManEd (machine-generated with subsequent human editing) generally received slightly over half the score (42%) compared to cases 03-AI and 04-AI (machine-generated with no further modifications; 74%). This reflects a typical scenario of student misconduct in cases where the use of AI is prohibited. The student obtains a text written by an AI and then quickly goes through it and makes some minor changes such as using synonyms to try to disguise unauthorised content generation. This type of writing has been called patchwriting (Howard 1995). Only ~ 50% accuracy of the classifiers shows that these cases, which are assumed to be the most common ones, are almost undetectable by current tools.

Influence of machine paraphrase

Probably the most surprising results are for case 06-Para (machine-generated with subsequent machine paraphrase). The use of AI to transform AI-generated text results in text that the classifiers consider human-written. The overall accuracy for this case was 26%, which means that most AI-generated texts remain undetected when machine-paraphrased.

Consistency in tool results

With the notable exception of GPT Zero, all the tested tools followed the pattern of higher accuracy when identifying human-written text than when identifying texts generated or modified by AI or machine tools, as seen in Fig. 3. Therefore, their classification is (probably deliberately) biased towards humans rather than AI output. This classification bias is preferable in academic contexts for the reasons discussed below.

Precision

Another important indicator of system's performance is precision, i.e. the ratio of true positive cases to all positively classified cases. Precision indicates the probability that a positive classification provided by the system is correct. For pure binary classifiers, the precision is calculated as a ratio of true positives to all positively classified cases:

$$\mathrm{Precision}=\mathrm{TP }/ (\mathrm{TP}+\mathrm{FP})$$

In case of partially true/false positives, the researches had two options how to deal with them. The exclusive approach counts them as negatively classified (so the formula does not change), whereas the inclusive approach counts them as positively classified:

$$\mathrm{Precision}\_\mathrm{incl}=(\mathrm{TP}+\mathrm{PTP}) / (\mathrm{TP}+\mathrm{PTP}+\mathrm{FP}+\mathrm{PFP})$$

Table 12 shows an overview of the classification results, i.e. all (partially) true/false positives/negatives. Also, both inclusive and exclusive precision values are provided. Precision is missing for Content at Scale because this system did not provide any positive classifications. The only system for which the inclusive precision is significantly different from the exclusive one, is GPT Zero which yielded the largest number of partially false positives.

Error analysis

In this section, the researchers quantify more indicators of tools' performance, namely two types of classification errors that might have significant consequences in educational contexts: false positives leading to false accusations against a student and undetected cases (students gaining an unfair advantage over others), i.e. false negative ratio which is tightly related to recall.

False accusations: harm to individual students

If educators use one of the classifiers to detect student misconduct, there is a question of what kind of output leads to the accusation of a student from unauthorised content generation. The researchers believe that a typical educator would accuse a student if the output of the classifier is positive or partially positive. Some teachers may also suspect students of misconduct in unclear or partially negative cases, but the research authors think that educators generally do not initiate disciplinary action in these cases. Therefore, for each tool, we also computed the likelihood of false accusation of a student as a ratio of false positives and partially false positives to all negative cases, i.e.

$$\mathrm{FPR}=(\mathrm{FP}+\mathrm{PFP}) /\mathrm{ N}\_\mathrm{negative}$$

Table 13 shows the number of cases in which the classification of a particular document would lead to a false accusation. The table includes only documents 01-Hum and 02-MT, because the AI-generated documents are not relevant. The risk of false accusations is zero for half of the tools, as can be also seen from Figs. 4 and 5. Six of the fourteen tools tested generated false positives, with the risk increasing dramatically for machine-translated texts. For GPT Zero, half of the positive classifications would be false accusations, which makes this tool unsuitable for the academic environment.

Undetected cases: undermining academic integrity

Another form of academic harm is undetected cases, i.e. AI-generated texts that remain undetected. A student who used unauthorised content generation likely obtains an unfair advantage over those who fulfilled the task with integrity. The actual victims of this form of misconduct are the honest students that receive the same credits as the dishonest ones. The likelihood of an AI-generated document being undetected (false negative rate, FNR) is given in Table 14, which includes only positive cases (03-AI, 04-AI, 05-ManEd and 06-Para). The false negative rate is calculated as

$$\mathrm{FNR}=(\mathrm{FN}+\mathrm{PFN}) /\mathrm{ N}\_\mathrm{positive}$$

For the sake of completeness, Table 14 also contains recall (1-FNR) that indicates how many of positive cases were correclty classified by the system.

Figures 6, 7, and 8 above show that 13 out of the 14 tested tools produced false negatives or partially false negatives for documents 03-AI and 04-AI; only Turnitin correctly classified all documents in these classes. None of the tools could correctly classify all AI-generated documents that undergo manual editing or machine paraphrasing.

As the document sets 03-AI and 04-AI were prepared using the same method, the researchers expected the results would be the same. However, for some tools (OpenAI Text Classifier and DetectGPT), the results were notably different. This could indicate a mistake in testing made or interpretation of the results. Therefore, the researchers double-checked all the results to avoid this kind of mistake. We also tried to upload some documents again. We did obtain different values, but we found out that this was due to inconsistency in the results of these tools and not due to our mistakes.

Content at Scale misclassified all of the positive cases; these results in combination with the 100% correct classification of human-written documents indicate that the tool is inherently biased towards human classification and thus completely useless. Overall, of the AI-generated texts approx. 20% of cases would likely be misattributed to humans, meaning the risk of unfair advantage is significantly greater than that of false accusation.

Figures 9 and 10 show an even greater risk of students gaining an unfair advantage through the use of obfuscation strategies. At an overall level, for manually edited texts (case 05-ManEd) the ratio of undetected texts increases to approx. 50% and in the case of machine-paraphrased texts (case 06-Para) rises even higher.

Usability issues

There were a few usability issues that cropped up during the testing that may be attributable to the beta nature of the tools under investigation.

For example, the tool DetectGPT at some point stopped working and only replied with the statement "Server error 😕 We might just be overloaded. Try again in a few minutes? ". This issue occurred after the initial testing round and persisted until the time of submission of this paper. Others would stall in an apparent infinite loop or throw an error message and the test had to be repeated at a later time.

Writeful GPT Detector would not accept computer code. The tool apparently identified code as not English, and the tool only accepted English texts.

Compilatio at one point returned "NaN% reliability" (See Fig. 11) for a ChatGPT-generated text that included program code. "NaN" is computer jargon for "not a number" and indicates that there were calculation issues such as division by zero or number representation overflow. Since there was also a robot head returned, this was evaluated as correctly identifying ChatGPT-generated text, but the non-numerical percentage might confuse instructors using the tool.

The operation of a few of the tools was not immediately clear to some of the authors and the handling of results was sometimes not easy to document. For example, in PlagiarismCheck the AI-Detection button was not always presented on the screen and it would only show the last four tests done. Interestingly, Turnitin often returned high similarity values for ChatGPT-generated text, especially for program code or program output. This was distracting, as the similarity results were given first, the AI-detection could only be accessed by clicking on a number above the text "AI" that did not look clickable, but was, see Fig. 12.

Discussion

Detection tools for AI-generated text do fail, they are neither accurate nor reliable (all scored below 80% of accuracy and only 5 over 70%). In general, they have been found to diagnose human-written documents as AI-generated (false positives) and often diagnose AI-generated texts as human-written (false negatives). Our findings are consistent with previously published studies (Gao et al. 2022; Anderson et al. 2023; Elkhatat et al. 2023; Demers 2023; Gewirtz 2023; Krishna et al. 2023; Pegoraro et al. 2023; van Oijen 2023; Wang et al. 2023) and substantially differ from what some detection tools for AI-generated text claim (Compilatio 2023; Crossplag.com 2023; GoWinston.ai 2023; Zero GPT 2023). The detection tools present a main bias towards classifying the output as human-written rather than detecting AI-generated content. Overall, approximately 20% of AI-generated texts would likely be misattributed to humans.

They are neither robust, since their performance worsens even more with the use of obfuscation techniques such as manual editing or machine paraphrasing, nor are they able to cope with texts translated from other languages. Overall, approximately 50% of AI-generated texts that undergo some obfuscation would likely be misattributed to humans.

The results provided by the tools are not always easy to interpret for an average user. Some of them provide statistical information to justify the classification, and others highlight the text that is "likely" machine-generated. Some present values such as "perplexity = 137.222" or "Burstiness Score: 17104.959" with many digits of precision that do not generally help a user understand the results.

Some of the detection tools such as Writer are clearly aimed to be used to hide AI-written text, providing suggestions to users such as "You should edit your text until there's less detectable AI content." (See Fig. 13).

Detection tools for AI-generated text provide simple outputs with statements like "This document was likely written by AI" or "11% likely this comes from GPT-3, GPT-4 or ChatGPT", without any possibility of verification or evidence. Therefore, a student accused of unauthorised content generation only on this basis would have no possibility for a defence. The probability of false positives ranged from 0% (Turnitin) to 50% (GPT Zero). The probability of false negatives ranged from 8% (GPT Zero) to 100% (Content at Scale). The different types of failures may have serious implications. False positives could lead to wrong accusations of students, the false negatives allow students to evade detection of unauthorised content generation gaining unfair advantages and promoting impunity. Our experience and personal communications indicate that there is a large group of academics that believe in the output of the classifiers. The research results show that users should be extremely cautious when interpreting the results.

It is noteworthy that using machine translation such as Google translate or DeepL can lead to a higher number of false positives, leaving L2 students (and researchers) at risk of being falsely accused of unauthorised content generation when using machine translation to translate their own texts.

As the tools do not provide any evidence, the likelihood that an educational institution is able to prove this form of academic misconduct is extremely low. Reports provided by detection tools for AI-generated text cannot be used as the only basis for reporting students for cheating. They can give faculty a hint that some sort of misconduct may have happened, but further dialogue and conversations with students should take place.

One of the tools that the researchers came across, GLTR ( http://gltr.io/) does not provide any classification, so it was decided to exclude it from testing. Nonetheless, it highlights the words (tokens) based on how commonly they appear in a given context. Interpretation of the output is up to the educator, but the research authors find the visualisation of this information very useful. The colour-coded predictability of individual words does not necessarily mean that the text was generated by AI, but may also mean that the text does not bring any innovation or added value, which might be-in some situations-a relevant indicator of its quality.

As the detection tools for AI-generated text are not reliable, a prevention-focused approach needs to be prioritised over a detection one. It is also paramount to inform the educators about this fact. The focus should instead be on the preventive pedagogical strategies on how to ethically use generative AI tools, including a discussion about the benefits and limitations of such tools.

This presupposes defining, describing, and training on the differences between the ethical and unethical use of AI tools will be important for students, faculty, and staff. The ENAI recommendations on the ethical use of Artificial Intelligence in Education may be a good starting point (Foltýnek et al. 2023) for such discussions. It is also important to encourage educators to rethink their assessment strategies and instruments to achieve a design with features that reduce or even eliminate the possibility of enabling cheating.

Our study has some limitations. It focused only on English language texts. Even though we had computer code, we did not test the performance of the systems specifically on that. There were also indications that the results from the tools can vary when the same material is tested at a different time; we did not systematically examine the replicability of the results provided by the tools. Nevertheless, we tentatively suggest that this inconsistency can have major implications in misconduct investigations and thus provides another strong reason against the use of these tools as a single source of an accusation of misconduct. Our document set is also somewhat limited: we did not test the kind of hybrid writing with iterative use of AI that may be likely to be more typical of student use of generative AI. However, the poor performance of the tools across the range of documents does not imply better performance for hybrid writing.

Conclusion and future work

This paper exposes serious limitations of the state-of-the-art AI-generated text detection tools and their unsuitability for use as evidence of academic misconduct. Our findings do not confirm the claims presented by the systems. They too often present false positives and false negatives. Moreover, it is too easy to game the systems by using paraphrasing tools or machine translation. Therefore, our conclusion is that the systems we tested should not be used in academic settings. Although text matching software also suffers from false positives and false negatives (Foltýnek et al. 2020), at least it is possible to provide evidence of potential misconduct. In the case of the detection tools for AI-generated text, this is not the case.

Our findings strongly suggest that the "easy solution" for detection of AI-generated text does not (and maybe even could not) exist. Therefore, rather than focusing on detection strategies, educators continue to need to focus on preventive measures and continue to rethink academic assessment strategies (see, for example, Bjelobaba 2020). Written assessment should focus on the process of development of student skills rather than the final product.

Future research in this area should test the performance of AI-generated text detection tools on texts produced with different (and multiple) levels of obfuscation e.g., the use of machine paraphrasers, translators, patch writers, etc. Another line of research might explore the detection of AI-generated text at a cohort level through its impact on student learning (e. g. through assessment scores) and education systems (e. g. the impact of generative AI on similarity scores). Research should also build on the known issues with cloud-based text-matching software to explore the legal implications and data privacy issues involved in uploading content to cloud-based (or institutional) AI detection tools.

Availability of data and materials

Abbreviations

01-Hum:

Human-written

02-MT:

Human-written in a non-English language with a subsequent AI/machine translation to English

03-AI:

AI-generated text

04-AI:

AI-generated text with subsequent human manual edits

05-ManEd:

AI-generated text with subsequent manual paraphrase by human

06-Para:

AI-generated text with subsequent AI/machine paraphrase

ACC:

Accuracy

ACC_bin:

Accuracy, binary approach

ACC_SEMIBIN:

Accuracy, semi-binary approach

AI:

Artificial intelligence

GPT:

Generative pre-trained transformer

FAS:

False accusation

FN:

False negative

FP:

False positive

HEIs:

Higher education institutions

LLM:

Large language models

NaN:

Not a number

PFN:

Partially false negative

PFP:

Partially false positive

PTP:

Partially true positive

PTN:

Partially true negative

TN:

True negative

TP:

True positive

UNC:

Unclear

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Acknowledgements

The authors wish to thank their colleague Július Kravjar from Slovakia who contributed a full set of test documents to the investigation.

The authors also wish to thank their colleagues from Turkey, Salim Razı and Özgür Çelik, who participated in the initial stages of the discussions about this research endeavour, but due to the devastating earthquake in February 2023 were not able to contribute further.

The tool similarity-texter was created as part of the bachelor's thesis of Sofia Kalaidopoulou and is based on Dick Grune's sim_text algorithm. It was submitted to the HTW Berlin in 2016 and is available under a Creative Commons BY-NC-SA 4.0 International License at https://people.f4.htw-berlin.de/~weberwu/simtexter/app.html.

ChatGPT was NOT used to tweak any portion of this publication.

Funding

Open access funding provided by Uppsala University. The authors had no funding for this research other than from their respective institutions.

Author information

Authors and Affiliations

1. University of Applied Sciences HTW, Berlin, Germany

Debora Weber-Wulff

2. Riga Technical University, Rīga, Latvia

Alla Anohina-Naumeca

3. Uppsala University, Uppsala, Sweden

Sonja Bjelobaba

4. Masaryk University, Brno, Czechia

Tomáš Foltýnek & Petr Šigut

5. Universidad de Monterrey, San Pedro Garza García, Mexico

Jean Guerrero-Dib

6. Queen Mary University of London, London, UK

Olumide Popoola

7. University of Leeds, Leeds, UK

Lorna Waddington

Contributions

All authors created test data, ran the tests, collected data, discussed the statistical results, and contributed equally to the text. TF and OP prepared the statistics for discussion.

Authors' information

The authors are members of the European Network for Academic Integrity (ENAI) working group on Technology and Academic Integrity. DWW is a plagiarism researcher and a retired professor of computer science from the HTW Berlin, Germany. AAN is an associate professor at the Department of Artificial Intelligence and Systems Engineering of Riga Technical University, Latvia. SB is a researcher in research integrity at Center for Research Ethics & Bioethics, at Uppsala University, Sweden, and the Vice-president of ENAI. TF is an assistant professor at the Department of Machine Learning and Data Processing at the Faculty of Informatics, Masaryk University, Czechia, and President of ENAI. JGD is a professor of the School of Engineering from University of Monterrey, Mexico and oversees the efforts of its Center for Integrity and Ethics. OP is an Education Developer specialising in assessment integrity at Queen Mary University of London, UK. PS is a student of Computer Science at the Faculty of Informatics, Masaryk University, Czechia. LW is the University of Leeds, UK, Academic Integrity Lead.

Corresponding author

Correspondence to Sonja Bjelobaba.

Ethics declarations

Competing interests

Two authors of this article, SB and TF, are involved in organising the European Conference on Ethics and Integrity in Academia 2023, co-organised by the European Network for Academic Integrity. This conference receives sponsorship from Turnitin and Compilatio. This did not influence the research presented in the paper in any phase.

Three of the authors, JGD, SB and TF are members of the editorial board of the International Journal for Educational Integrity. They can thus not act as reviewers.

One author, TF, is guest editor for the special issue on Artificial Intelligence.

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Supplementary Information

Appendix

Appendix

Case studies 05-ManEd

The following images show the generated texts on the left and the human-obfuscated ones on the right. The identical text is coloured in the same colour on both sides, with the changes popping out in white. The images were prepared using the similarity-texter. As can be seen, some texts were rather heavily re-written, others only had a few words exchanged.

Case studies 06-Para

These test cases were first generated with ChatGPT, then automatically re-written using Quillbot with the default settings. The generated original is on the left, the re-written version on the right.

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Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S. et al. Testing of detection tools for AI-generated text. Int J Educ Integr 19, 26 (2023). https://doi.org/10.1007/s40979-023-00146-z
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Keywords

# data[].attributes.attachment_link.title
Testing of detection tools for AI-generated text - International Journal for Educational Integrity

# data[].attributes.attachment_link.description
Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis.

# data[].attributes.headline
Stanford HAI: AI Detectors Biased Against Non-Native English Writers

# data[].attributes.subject
Stanford HAI: AI Detectors Biased Against Non-Native English Writers

# data[].attributes.body
Summarizes Stanford research finding that several detectors frequently misclassified writing by non-native English writers as AI-generated. It raises significant fairness concerns when detector scores are used in evaluative or disciplinary settings.

# data[].attributes.attachment_link.title
AI-Detectors Biased Against Non-Native English Writers

# data[].attributes.attachment_link.description
Don't put faith in detectors that are "unreliable and easily gamed," says scholar.

# data[].attributes.headline
Perkins et al.: Simple Techniques to Bypass Generative AI Text Detectors

# data[].attributes.subject
Perkins et al.: Simple Techniques to Bypass Generative AI Text Detectors

# data[].attributes.body
This peer-reviewed study found poor overall detection performance and demonstrated that relatively simple text-modification techniques could substantially reduce detection accuracy. It also identified false-positive risks for human-written control samples.

# data[].attributes.attachment_link.title
Simple techniques to bypass GenAI text detectors: implications for inclusive education - International Journal of Educational Technology in Higher Education

# data[].attributes.attachment_link.description
This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity concerns. Results show significant reductions in detector accuracy (17.4%) when faced with simple techniques to manipulate the AI generated content. The varying performances of GenAI tools and detectors indicate they cannot currently be recommended for determining academic integrity violations due to accuracy limitations and the potential for false accusation which undermines inclusive and fair assessment practices. However, these tools may support learning and academic integrity when used non-punitively. This study aims to guide educators and institutions in the critical implementation of AI text detectors in higher education, highlighting the importance of exploring alternatives to maintain inclusivity in the face of emerging technologies.

# data[].attributes.headline
Bordalejo et al.: The Impact of AI on Commonly Used Writing Tools and AI Detection

# data[].attributes.subject
Bordalejo et al.: The Impact of AI on Commonly Used Writing Tools and AI Detection

# data[].attributes.body
Examines how tools such as Grammarly, Writefull, and other writing assistants can affect detector results. The study is important because students may be flagged after using widely accepted editing or accessibility tools rather than using AI to generate an assignment.

# data[].attributes.attachment_link.title
“Scarlet Cloak and the Forest Adventure”: a preliminary study of the impact of AI on commonly used writing tools - International Journal of Educational Technology in Higher Education

# data[].attributes.attachment_link.description
This paper explores the growing complexity of detecting and differentiating generative AI from other AI interventions. Initially prompted by noticing how tools like Grammarly were being flagged by AI detection software, it examines how these popular tools such as Grammarly, EditPad, Writefull, and AI models such as ChatGPT and Microsoft Bing Copilot affect human-generated texts and how accurately current AI-detection systems, including Turnitin and GPTZero, can assess texts for use of these tools. The results highlight that widely used writing aids, even those not primarily generative, can trigger false positives in AI detection tools. In order to provide a dataset, the authors applied different AI-enhanced tools to a number of texts of different styles that were written prior to the development of consumer AI tools, and evaluated their impact through key metrics such as readability, perplexity, and burstiness. The findings reveal that tools like Grammarly that subtly enhance readability also trigger detection and increase false positives, especially for non-native speakers. In general, paraphrasing tools score low values in AI detection software, allowing the changes to go mostly unnoticed by the software. However, the use of Microsoft Bing Copilot and Writefull on our selected texts were able to eschew AI detection fairly consistently. To exacerbate this problem, traditional AI detectors like Turnitin and GPTZero struggle to reliably differentiate between legitimate paraphrasing and AI generation, undermining their utility for enforcing academic integrity. The study concludes by urging educators to focus on managing interactions with AI in academic settings rather than outright banning its use. It calls for the creation of policies and guidelines that acknowledge the evolving role of AI in writing, emphasizing the need to interpret detection scores cautiously to avoid penalizing students unfairly. In addition, encouraging openness on how AI is used in writing could alleviate concerns in the research and writing process for both students and academics. The paper recommends a shift toward teaching responsible AI usage rather than pursuing rigid bans or relying on detection metrics that may not accurately capture misconduct.

# data[].attributes.headline
Cornell University: AI and Academic Integrity

# data[].attributes.subject
Cornell University: AI and Academic Integrity

# data[].attributes.body
Cornell does not recommend relying on automated AI detection algorithms for academic integrity violations because they are unreliable and cannot provide definitive evidence of misconduct. It recommends authentic assessment and trusting relationships rather than technology-centered policing.

# data[].attributes.attachment_link.readable_text
For more information on promoting academic integrity in your classroom, view our strategies.

Although the Artificial Intelligence (AI) landscape is evolving rapidly, one clear impact is that this technology is already being integrated into the academic lives of both students and faculty. As a result, all instructors should consider how they will provide clear guidelines concerning the use of generative AI in academic work for every class they teach.

Here are a few things to keep in mind when addressing AI and academic integrity in your course.

Clearly Communicate your Expectations

Students benefit from clearly communicated expectations for their coursework, and will likewise benefit from transparency regarding limits to and expectations for use of generative AI in their course assignments and exams.

To reduce the chance of violations of academic integrity, explicitly communicate your course's generative AI policies by:

Including in your syllabus clear expectations regarding the use of generative AI tools and any differentiation in the usage policy for specific assignments.

Clearly identify in what situations generative AI use is prohibited or permitted.

When generative AI is permitted, faculty should be clear about student expectations in terms of documentation and attribution, as well as what work is expected to be produced by the student themselves, and how the student is expected to validate or verify output from generative AI. See Crediting generative AI in an assignment, below.

Discussing the expectations spelled out in the syllabus during a class session.

Reviewing expectations and communicating how they apply to specific assignments.

Engaging in ongoing conversations about the importance of academic integrity, including the fact that basic academic integrity principles remain important and still apply regardless of the existence of generative AI tools.

Example Course Policy Language for Generative AI Use

Crediting Generative AI in an Assignment

When using material generated from an LLM in course materials or in assignments that students submit, transparency is key, and these instances should be properly referenced. When possible, you may want to select citation guidelines and share them with your students using the style guide appropriate to your discipline. Currently, the APA style guide, MLA style guide, and Chicago Manual of Style include recommendations and examples for citing LLM-generated materials that you can share with students.

For example, consider:

Including an in-text citation with reference to the LLM model, i.e. include text in quotes and reference the author, i.e. (OpenAI, 2023).

Asking students to include an AI-generated source in the methods section of their research paper.

Having students provide the text or prompt they used for the LLM to generate a response and include what LLM model, date, and version they used.

Similarly, if instructors choose to use LLMs to help in the preparation of course materials, such work should be acknowledged and attributed.

If you have specific questions and would like to schedule an individual or group consultation, please contact us.

Detecting AI-Generated Content

Generative AI does provide an increased risk that students may use it to submit work that is not their own. It is tempting to seek technology-based solutions to identify inappropriate use of generative AI tools such as ChatGPT.

Unfortunately, it is unlikely that detection technologies will provide a workable solution to this problem. It can be very difficult to accurately detect AI-generated content. Although some instructors have reported that the tone and feel of AI-generated content is different from the student work they normally receive, in some cases it is almost undetectable from the work produced by students. Detection tools claim to identify work as AI generated, but cannot provide evidence for that claim, and tests have revealed significant margins of error. This raises a substantial risk that students may be wrongly accused of improper use of generative AI.

We currently do not recommend using current automatic detection algorithms for academic integrity violations using generative AI, given their unreliability and current inability to provide definitive evidence of violations. We believe that establishing trusting relationships with students and designing authentic assessments will likely be far more effective than policing students.

# data[].attributes.attachment_link.title
AI & Academic Integrity | Center for Teaching Innovation

# data[].attributes.attachment_link.description
Although the Artificial Intelligence (AI) landscape is evolving rapidly, one clear impact is that this technology is already being integrated into the academic lives of both students and faculty. As a result, all instructors should consider how they will provide clear guidelines concerning the use o…

# data[].attributes.body
Explains the risks of high error rates, false accusations, and overconfidence in detector outputs. It recommends clearer AI policies, conversations with students, assignment redesign, and examination of students’ learning processes instead.

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MIT Sloan Teaching & Learning Technologies

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AI Detectors Don’t Work. Here’s What to Do Instead. - MIT Sloan Teaching & Learning Technologies

# data[].attributes.attachment_link.description
As new tools raise concerns around AI academic integrity, instructors can deter misuse through transparent policies, open discussions with students, assignments that engage intrinsic motivation, and inclusive teaching practices.