CEL facilitates multi-institutional research on engaged learning topics. Participants from institutions around the world collaborate over three years, producing scholarship that shapes research and practice globally.
CEL is home to two book series. In addition, CEL research seminars and other initiatives have produced 100+ publications (to date).
CEL’s concise guides offer research-informed practices for engaged learning.
CEL’s concise guides offer practical strategies for studying engaged learning.
CEL brings together international leaders in higher education to develop, synthesize, and share rigorous research on central questions about student learning.
The CEL Scholar role and CEL Student Scholars program enable Elon faculty and students to deepen their understanding of and professional development in scholarly activity on engaged learning.
Asprovska, Marijana, and Nathan Hunter. 2024. "The Tokenization Problem: Understanding Generative AI’s Computational Language Bias." Ubiquity Proceedings 4 (1). https://doi.org/10.5334/uproc.123.
The paper explains the process of tokenization in large language models and examines its impact on text outputs across different languages.
Recommended by Nora Rivera.
Abrams, Samuel J. 2026. "AI Didn’t Break the University. It Revealed What Was Already Broken." RealClearEducation, May 31, 2026. https://www.realcleareducation.com/articles/2026/05/31/ai_didnt_break_the_university_it_revealed_what_was_already_broken_1185867.html.
The article centers around the theory that higher ed has lost its way. The design of things like online courses has degraded the learning experience to the point where the chatbot seems like a good use of a student’s time. “The formation-centered model is rare and expensive because institutions chose, over decades, to make it so. The lecture hall replaced the seminar, the learning management system replaced the lecture hall, asynchronous video replaced the live lecture, and now the chatbot is replacing the cognitive work itself. Each step was sold as an efficiency that would preserve the educational core.”
Recommended by Dawn Zimmerer.
Alamray, Fawzia, Naif R Aljohani, Ahmed S. Alfakeeh, Nawaf Alhebaishi, and Dragan Gašević. 2026. "Exploring Students’ Cognitive Engagement with Generative Artificial Intelligence." LAK '26: Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference: 709-720. https://dl.acm.org/doi/pdf/10.1145/3785022.3785132.
This article demonstrates that AI tools can teach students to engage higher cognitive processes. The tool itself prompts students to transition from a Bloom’s Understand or Evaluate to Analyze and Apply.
Recommended by Ed Gehringer
Jisc. 2025. Student Perceptions of AI 2025. Jisc: Jisc. https://www.jisc.ac.uk/reports/student-perceptions-of-ai-2025.
This report from JISC in the UK provides an interesting snapshot of student perceptions of GenAI between 2024-2025. It highlights how students have been using GenA tools in their learning, but also concerns that students have shared.
Recommended by Harry West
Basgier, Christopher, and Lydia Wilkes. 2025. "From an Unsettled Middle: A Critical-Ethical Stance for GenAI-Engaged Writing Assignments." College Composition and Communication 77 (1): 62-88. https://doi.org/10.58680/ccc202577162.
I’m recommending this piece for its critical thinking framework, which offers a flexible way to think about GenAI pedagogy across disciplinary contexts. At the same time, I want to keep pressing on its central tension: whether the “unsettled middle” it advocates is genuinely unsettled, or whether it sometimes functions as integration pedagogy built on disguised refusal.
Recommended by Travis Maynard
Bearman, Margaret, Juliana Ryan, and Rola Ajjawi. 2023. "Discourses of Artificial Intelligence in Higher Education: A Critical Literature Review." Higher Education 86 (2): 369-385. https://pubsonline.informs.org/doi/10.1287/orsc.2025.21838.
Recommended by Harry West.
Burns, Mary, Rebecca Winthrop, Natasha Luther, Emma Venetis, and Rida Karim. 2026. A New Direction for Students in an AI World: Propser, Prepare, Protect. Center for Universal Education at The Brookings Institute.
This report argues that AI can enrich learning when it is carefully designed, pedagogically grounded, and used with safeguards, but that current risks—especially cognitive development, social-emotional development, trust, safety, dependence, and equity—need urgent attention.
Recommended by Tom Ritchie
Recommended by Robyn Edwards
Corbin, Thomas, Jack Walton, Peter Bannister, and Deranty Jean-Philippe. 2026. "On the Essay in a Time of GenAI." Educational Philosophy and Theory 58 (3): 198-210. https://doi.org/10.1080/00131857.2025.2572802.
This well-known article argues that the current crisis in academic writing is not merely a technological issue caused by the emergence of GenAI, but part of an already entrenched practice of treating essays as standardised, measurable products. By reducing writing to rigid evaluation criteria and obscuring the often messy, deeply human process of intellectual discovery via writing, education institutions have inadvertently created a format that GenAI can easily replicate.
Recommended by Mary Beth Benbenek
Corbin, Thomas, Phillip Dawson, Kelli Nicola-Richmond, and Helen Partridge. 2025. "‘Where’s the Line? It’s an Absurd Line’: Towards a Framework for Acceptable Uses of AI in Assessment." Assessment Evaluation in Higher Education 50 (5): 705-717. https://doi.org/10.1080/02602938.2025.2456207.
Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University: Yale University Press.
Recommended by Tom Ritchie.
Dell'Acqua, Fabrizio, Edward McFowland lll, Ethan Mollick, Hila Lifshitz, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. 2026. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality." Organization Science 37 (2): 403-423. https://doi.org/10.1287/orsc.2025.21838.
Recommended by Subhadra Ganguli.
Dukes, Jesse. 2026. "AI Literacy Part II: What We Talk About When We Talk About AI Literacy." TeachLab with Justin Reich (Podcast), February 5, 2026. https://www.teachlabpodcast.com/ai-literacy-part-ii-what-we-talk-about-when-we-talk-about-ai-literacy/.
The guests on this podcast challenge the idea of “generalized AI Literacy and training,” in favor of creating opportunities for teachers to gain hands-on practice with AI that informs if/how it can support learning in specific, disciplinary contexts. [This podcast is an hour long; I listened to it on 2x speed.]
Recommended by Lisa Berry
Ennoure, Taoufik. 2026. "From Restriction to Integration: Practical Strategies for Embracing AI in Online Courses." Faculty Focus (blog), May 13, 2026. https://s39613.pcdn.co/articles/online-education/from-restriction-to-integration-practical-strategies-for-embracing-ai-in-online-courses/.
A brief write-up that I found helped me continue thinking about how to best integrate AI-related assessments and assignments in online and asynchronous assessments in the AI world.
Recommended by Jason Beasley
Frank, Daniel, and Jennifer K. Johnson. 2025. "Drafting a Policy for Critical Use of AI Writing Technologies in Higher Education." In Composing with AI, edited by Nupoor Ranade and Douglas Eyman, Utah State University Press: Utah State University Press.
Nice piece that focuses on how a program developed an AI policy.
Recommended by Neil Baird
Gerlich, Michael. 2025. "AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking." Societies 15 (1): 6. https://doi.org/10.3390/soc15010006.
This paper reports on a mixed-method study exploring the relationship between AI tool usage, cognitive offloading and critical thinking abilities – a very important area to consider for educators at all levels.
Recommended by Nadya Yakovchuk
Gin, Brian C., Patricia S. O’Sullivan, Karen E. Hauer, Raja-Elie Abdulnour, Madelynn Mackenzie, Olle Ten Cate, and Christy K Boscardin. 2025. "Entrustment and EPAs for Artificial Intelligence (AI): A Framework to Safeguard the Use of AI in Health Professions Education." Academic Medicine 100 (3): 264-272. https://pubmed.ncbi.nlm.nih.gov/39761533/.
Recommended by E’lise Nissen.
Goodman, Nathan. 2022. "Economic Education and AI." Econlib (blog), December 11, 2022. https://www.econlib.org/economic-education-and-ai/.
This talks about various ways to use ChatGPT in the classroom to break the myth that it has all the answers for students. This applies to teaching, as if we are not careful, then we can be embarrassed in class if we are using ChatGPT without proper verification.
Recommended by Subhadra Ganguli
Gupta, Anuj, Yasser Atef, Anna Mills, and Maha Bali. 2024. "Assistant, Parrot, or Colonizing Loudspeaker? ChatGPT Metaphors for Developing Critical AI Literacies." Open Praxis 16 (1): 37-53. https://doi.org/10.55982/openpraxis.16.1.631.
Han, Xiaoli, Hongchao Peng, and Mingzhuo Liu. 2025. "The Impact of GenAI on Learning Outcomes: A Systematic Review and Meta-Analysis of Experimental Studies." Educational Research Review 48: article 100714. https://doi.org/10.1016/j.edurev.2025.100714.
This study examined 68 experimental studies on the use of generative AI tools such as ChatGPT, Claude, and Gemini in education. The researchers found that AI generally has a positive effect on student learning. The results suggest that AI can be an effective learning tool, but its impact varies depending on factors such as the student’s educational level, the subject, how the technology is used, etc. Overall, the study concludes that while generative AI shows significant promise in education, more research is needed to determine the most effective ways to integrate it into different learning environments.
Annotation by Chiebuka Tor
Findings indicate that students are using AI more and more. As educators we need rethink our pedagogy with these findings in mind.
Annotation by Kriss Kempt Graham
Handshake. 2026. AI and the Workforce Ahead: What the Class of 2026 Tells Us about the Future of the Labor Market. Online: Handshake. https://joinhandshake.com/themes/handshake/dist/assets/downloads/network-trends/%5BHNT%2026%5D%20Class%20of%202026.pdf.
I’m recommending this report as it shows AI readiness is increasingly becoming a workplace expectation, but also points to a clear mismatch: students know they need these skills, but many say their programs are not preparing them to use AI well.
Recommended by Travis Maynard.
Hasse, Alexa, Sandra Cortesi, Andres Lombana-Bermudez, and Urs Gassner. 2019. "Youth and Artificial Intelligence: Where We Stand ." Berkman Klein Center Research Publication 2019 (3). http://dx.doi.org/10.2139/ssrn.3385718.
I have found this resource helpful because it frames young people not only as users of AI, but as learners, creators, and social actors whose experiences with AI are shaped by education, creativity, work, privacy, and well-being. For me, it has been especially useful in thinking about engaged learning in the age of GenAI as something broader than academic integrity or tool use. It pushes me to ask how students can be supported in developing agency, critical awareness, and creative capacity as AI becomes part of their everyday lives.
Recommended by Alexander Eden
James, Trixie, John Pike, Grant Andrews, James Valentine, Michael Brickhill, Julie Sharp, and Tamra Ulpen. 2025. "Reluctant, Ambivalent and Progressive: Educators’ Attitudes towards the Use of Generative Artificial Intelligence in Enabling Education." Journal of University Teaching and Learning Practice 22 (10): 1-24. https://doi.org/10.53761/40w8jg17.
By showing how educators’ views of generative AI range from resistance to cautious experimentation, the study highlights how engaged learning in AI-rich environments depends not only on technology itself, but on instructors’ willingness to create learning experiences that support student autonomy, dialogue, and meaningful participation.
Recommended by Breana Bayraktar
Kestin, Greg, Kelly Miller, Anna Klales, Timothy Milbourne, and Gregorio Ponti. 2025. "AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting." Scientific Reports 15 (1): 17458. https://doi.org/10.1038/s41598-025-97652-6.
Recommended by Daniel Ernst.
Kofinas, Alexander K., Crystal Han-Huei Tsay, and David Pike. 2025. "The Impact of Generative AI on Academic Integrity of Authentic Assessments within a Higher Education Context." British Journal of Educational Technology 56 (6): 2522-2549. https://doi.org/10.1111/bjet.13585.
This article challenges the assumption that authentic assessments alone can protect against inappropriate AI use. The authors argue that higher education should place greater emphasis on assessments grounded in social, experiential, and performative learning, shifting attention from the production of written outputs toward active participation, interaction, and demonstration of learning.
Recommended by Jason Beasley.
Korinek, Anton. 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists." Journal of Economic Literature 61 (4): 1281-1317. https://doi.org/10.1257/jel.20231736.
Leahy, Sean, and Andrew Maynard. 2025. "Agentic AI in Education and the Art of Becoming." Modem Futura (Podcast), August 26, 2025. https://podcasts.apple.com/us/podcast/agentic-ai-in-education-the-art-of-becoming/id1771688480?i=1000723563234.
This episode reframes the question from what AI does to learning, to what learning is for. Drawing on Dewey and Papert, it argues for students building with AI rather than being optimised by it, and the distinction between ‘becoming better’ and simply ‘becoming’ is a provocation worth sitting with.
Long, Dong Yu, Shuai Wang, Sabariah Md Rashid, and Xiao Tao. 2025. "Artificial Intelligence in Higher Education (AIHE): A Systematic Review of Their Impact on Student Engagement and the Mediating Role of Teaching Methods." Frontiers in Education 10. https://doi.org/10.3389/feduc.2025.1648661.
By using the systematic review, this paper has similar advantages to multi-site research including a variety of topic areas and types of institutions and learners. The variety of reporting strategies including network charts and word clouds offers some accessible ways to think about the scope of what has been tested and can enable good discussion of the affordances of different research methods.
Annotation by Amanda Sturgill
I recommend this scholarly text because it’s a systematic review of recent work. It will help establish a baseline for conversations on specific research foci that the seminar participants will discuss. The article also introduces a framework for understanding the use of AI for student engagement.
Annotation by Trent M. Kays
Madsen Hardy, Sarah, Pary Fassihi, Shuang Geng, Christopher McVey, and Matt Parfitt. 2026. "Generative AI Use in College Writing Classes: An Analysis of Student Chat Logs and Writing Projects." Journal of Writing Research 17 (3). https://www.jowr.org/jowr/article/view/1762.
Recommended by Trent M. Kays.
Marino, Mark C. 2025. "ChatGPT Is Not Your Friend: The Importance of AI Literacy for Inclusive Writing Pedagogy." In Composing with AI, edited by Nupoor Ranade and Douglas Eyman, Utah State University Press: Utah State University Press.
Nice piece that argues for AI literacy in writing instruction.
Recommended by Neil Bard
McClain, Colleen, Monica Anderson, Olivia Sidoti, and William Bishop. 2026. "How Teens Use and View AI." Pew Research Center. February 24, 2026. https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/.
This report has been useful because it provides a current snapshot of how teens are actually using and thinking about AI, including school-related uses, confidence, concerns, and parent perspectives. I appreciate that it helps ground conversations about GenAI in young people’s lived realities rather than abstract speculation. For engaged learning, it reminds me that many students are already encountering AI outside formal learning spaces, so educators need to design learning experiences that are transparent, critical, creative, and responsive to students’ existing practices.
This is based on data that will quickly become dated, but the report has real value in two areas. First, knowing how our future students use the tools has value. I especially appreciate that the sample was large enough to have findings representing diverse types of students. Second, the types of questions they answered might give some ideas to teams in their research development.
Recommended by Amanda Sturgill
Mills, Jennie, Tina Beynen, Ivy May, Rachel Fitzgerald, Kimberly A. Hall, Evelyn Lai, Jon Mason, and Samantha Newell. 2025. "Re-Thinking SoTL for the Age of GenAI: Diffracted, Entangled, and Human." Teaching & Learning Inquiry 13: 1-19. https://doi.org/10.20343/teachlearninqu.13.49.
The article argues that instructors should move beyond simply reflecting on how generative AI affects teaching and learning. Instead, it asks us to look at how students, instructors, technology, assignments, policies, and campus culture all shape one another. It offers practical questions and starting points to help educators think through how GenAI is changing higher education and how we can respond in thoughtful, ethical, and useful ways.
Recommended by Dawn Zimmerer
Mohr, Sandra. 2026. "From AI Fatigue to AI Fluency." Chief Learning Officer, June 1, 2026. https://www.chieflearningofficer.com/2026/06/01/from-ai-fatigue-to-ai-fluency/.
Technology adoption is outpacing the learning systems we develop. This leads to AI Fatigue. AI Fusion skills, defined as “the human capabilities required to work effectively with generative AI—emphasizing judgment, problem framing, and accountability over technical proficiency alone,” can help solve that.
Organisation for Economic Co-operation and Development. 2026. OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education. Paris: OECD Publishing. https://www.oecd.org/en/publications/oecd-digital-education-outlook-2026_062a7394-en.html.
This report doesn’t concern itself with whether AI should or should not be used in education. Rather, it focuses on its effects on learning. I think the golden ticket idea it introduces is roughly this: the data shows that generally, on its own, AI use tends to improve short term results but hinder long term growth. How can we develop pedagogical strategies that enhance our skills with AI rather than replace them?
Annotation by Matthew Cornick
I recommend this piece because it offers a clear vision of GenAI as a dialogue-based pedagogical partner that supports learning through scaffolded questioning rather than simply giving students answers. I especially appreciate its emphasis on Socratic questioning, learner agency, and maintaining students’ cognitive engagement so that AI tutoring becomes a space for active meaning-making rather than passive consumption.
Annotation by Robyn Edwards
Organisation for Economic Co-operation and Development (OECD). 2026. OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education. OECD Publishing. https://www.oecd.org/en/publications/oecd-digital-education-outlook-2026_062a7394-en.html.
Overstreet, Matthew. 2022. "Writing as Extended Mind: Recentering Cognition, Rethinking Tool Use." Computers and Composition 63: 102700. https://doi.org/10.1016/j.compcom.2022.102700.
The development of an ecology extends the idea that it lives only within our mind but also in the tools we use.
Recommended by Djuddah Leijen.
Pallant, Jessica L., Janneke Blijlevens, Alexander Campbell , and Ryan Jopp. 2026. "Mastering Knowledge: The Impact of Generative AI on Student Learning Outcomes." Studies in Higher Education 51 (4): 714-735. https://doi-org.hindscc.idm.oclc.org/10.1080/03075079.2025.2487570.
The study shows that students who used GenAI to construct and augment knowledge, instead of in a procedural or regurgitative way, were more likely to achieve higher grades and demonstrate higher learning outcomes.
Singer, Jeremy, and Ethan Mollick. 2026. "Ethan Mollick | The Wharton School." The Education Equation with Jeremy Singer (Podcast), May 1, 2026. https://theeducationequation.org/episodes/ethan-mollick-wharton-school.
This podcast episode/interview is insightful and related to the other sources I recommended above. Mollick discusses how AI is changing education with Jeremy Singer of The College Board.
Recommended by Daniel Ernst
Stachowiak, Bonni. 2024. "Teaching with AI, with José Bowen." Teaching in Higher Ed, May 16, 2024. https://teachinginhighered.com/podcast/teaching-with-ai/.
Bowen’s conversation cuts right to the heart of what I have been wrestling with in my doc applied stats class. His argument that we need to stop policing AI and start redesigning assignments so that what students submit is evidence of their thinking, not just their output!
Recommended by Kriss Kemp Graham.
Stolpe, Karin, Andreas Larsson, and Marlene Johansson Falck. 2026. "Discipline-Specific AI Literacy (DiSAIL): A Theoretical Framework for Situated Engagement with Generative AI in Education." International Journal of Technology and Design Education 36: 1917–1932. https://doi.org/10.1007/s10798-026-10060-3.
This article introduces the Discipline-Specific AI Literacy (DiSAIL) framework, which emphasizes that AI literacy should be developed within the context of specific academic disciplines rather than through generic AI skills alone. The authors argue that meaningful engagement with genAI requires students to understand disciplinary knowledge, practices, and ethical considerations while using AI tools. This framework is a useful tool for higher education researchers and faculty seeking to integrate AI literacy into discipline-specific curricula and learning activities.
Annotation by Aaron Trocki
To prepare instructors and students to use AI effectively, we must attend to disciplinary cultures, norms and epistemologies. This article highlights ways to frame and navigate AI in disciplinary contexts.
Annotation by Lisa Berry
Wang, Shaofeng, and Hao Zhang. 2026. "Pedagogical Partnerships with Generative AI in Higher Education: How Dual Cognitive Pathways Paradoxically Enable Transformative Learning." International Journal of Educational Technology in Higher Education 23 (1): 11. https://link.springer.com/article/10.1186/s41239-026-00585-x.
Recommended by Robyn Edwards.
Weidlich, Joshua, Gašević Dragan, Drachsler Hendrik, and Kirschner Paul. 2025. "ChatGPT in Education: An Effect in Search of a Cause." Journal of Computer Assisted Learning 41 (5): e70105. https://doi.org/10.1111/jcal.70105.
Weng, Xiaojing, Qi Xia, Mingyue Gu, Kumaran Rajaram, and Thomas K.F. Chiu. 2024. "Assessment and Learning Outcomes for Generative AI in Higher Education: A Scoping Review on Current Research Status and Trends." Australasian Journal of Educational Technology 40 (6): 37-55. https://doi.org/10.14742/ajet.9540.
Wooldridge, Michael. 2025. "Generative AI: Where It Came From, What It Is, and What It Means for Education." Presentation at the International Technology, Education and Development (INTED) Conference International Technology, Education and Development (INTED) Conference, University of Oxford (UK), March 2025. https://iated.org/talks/Wooldridge_AI.
Michael Wooldridge is the Ashall Professor of Foundations of Artificial Intelligence at the University of Oxford. I recommend this talk because he provides a highly accessible description of Large Language Models characterising them as ‘productivity tools’ and ‘creativity assistants’ thus countering a lot of the hype about AI “understanding”. He also briefly describes practical implications for education in a way that is measured, practical and free of overstatement.
Yang, Huili. 2025. "Harnessing Generative AI: Exploring Its Impact on Cognitive Engagement, Emotional Engagement, Learning Retention, Reward Sensitivity, and Motivation Through Reinforcement Theory." Learning and Motivation 90: 102136. https://doi.org/10.1016/j.lmot.2025.102136.
Young, Jeff. 2026. "Tough Lessons College ‘AI Czars’ Have Learned." Learning Curve, May 26, 2026. https://learningcurve.fm/episodes/tough-lessons-college-ai-czars-have-learned.
The whole episode is compelling, but I was especially struck by the following section, which describes the work that all faculty and departments need to be doing: Decide how GenAI _should_ be used in your discipline, and then work to change curriculum in that direction. We cannot wait to be told what direction to go.
Recommended by Jeff Loats
Zhang, Wenkang, Albert W. Li, and Chenze Wu. 2025. "University Students’ Perceptions of Using Generative AI in Translation Practices." Instructional Science 53 (4): 633-655. https://doi.org/10.1007/s11251-025-09705-y.
