Bibliography Category: GenAI

  • 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.

    About this Journal Article:

    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.

  • 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.

    About this Journal Article:

    This was one of the first papers I engaged with regarding GenAI in higher education a few years ago. I found it a really helpful overview of existing literature on GenAI (it would now be interesting to see what a review like this would look like a few years on). Key themes include definitions of AI, the need for HE to change and adapt and what this might mean for authority, ethics and L&T practice.

    Recommended by Harry West.

  • 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.

    About this Journal Article:

    I found this an interesting paper highlighting the challenges in defining ‘acceptable’ or ‘appropriate’ uses of GenAI in assessment. Highlighting that students can form their own ethical frameworks meanwhile teaching staff find this very challenging and uncertain.

    Recommended by Harry West

  • Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University: Yale University Press.

    About this Book:

    Crawford reframes AI as infrastructure rather than technology, asking what it costs the planet and who bears those costs. For anyone rethinking engaged learning in this context, that shift in register, from pedagogy to political economy, is a necessary provocation, and one that sits in productive tension with government-level AI policy frameworks like the US AI Action Plan and the EU AI Act.

    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.

    About this Journal Article:

    Dell’Acqua et al. (2026) show that generative AI can significantly improve knowledge-worker productivity and quality for tasks within AI’s “jagged technological frontier,” with consultants using GPT-4 completing more tasks, working faster, and producing higher-quality outputs. However, for tasks outside AI’s capability frontier, AI use reduced correctness, showing that human judgment, task awareness, and careful supervision remain essential in human–AI collaboration. It will be interesting to think of something similar in the teaching and learning.

    Recommended by Subhadra Ganguli.

  • 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/.

    About this Journal Article:

    This article applies the Entrustable Professional Activities (EPA) framework—the dominant competency-based assessment model in U.S. medical education—to AI tools, proposing that we assess AI across ability, integrity, and benevolence just as we assess trainees. It operationalizes the novice-to-expert trajectory by defining when students are ready to be ‘entrusted’ to use AI independently in high-stakes clinical settings, directly addressing how assessment can measure progression in both human competency and human-AI collaboration.

    Recommended by E’lise Nissen.

  • 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.

    About this Journal Article:

    The metaphors framing in this paper is a genuinely useful provocation for thinking about student agency in AI-assisted learning, and the equity and coloniality dimensions map well onto the engaged learning context.

    Recommended by Tom Ritchie.

  • 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.

    About this Journal Article:

    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

  • 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.

    About this Journal Article:

    This study is interesting because it attempts to isolate the medium of educational delivery, comparing whether traditional active learning classrooms or AI models can teach content better. It is also a randomized controlled trial, which is a strong method.

    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.

    About this Journal Article:

    The study provides empirical evidence that certain forms of assessment (e.g., authentic assessment) are not a sufficient safeguard against inappropriate AI usage. It has helped continue to shape my view of how to design and integrate AI into a course in a way that reinforces learning instead of substituting away from it.

    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.

    About this Journal Article:

    This article can be used in teaching a well as research. Since the workshop will be at the intersection of the two – researching about learning I wanted to have this showcased. Korinek argues that recent advances in large language models have made them powerful tools for economic research, especially for brainstorming, writing, coding, data analysis, literature support, mathematical reasoning, and research promotion. He emphasizes that LLMs can substantially improve researcher productivity, but their use still requires human judgment, verification, transparency, and caution because of risks such as hallucinations, bias, privacy concerns, and reproducibility problems.

    Recommended by Subhadra Ganguli.

  • 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.

    About this Journal Article:

    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.

    About this Journal Article:

    This is an excellent research study of chatbot use in writing courses. GenAI and writing is a topic of much concern in my field, and it will be beneficial for colleagues to understand how writing researchers are understanding the generation of text through GenAI.

    Recommended by Trent M. Kays.

  • 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/.

    About this Blog Post:

    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.

    Annotation by Amanda Sturgill

    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.

    Annotation by Alexander Eden

  • 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.

    About this Report:

    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

  • 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.

    About this Journal Article:

    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.

  • 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.

    About this Journal Article:

    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.

    About this Journal Article:

    I recommend this article because it offers a more nuanced way to think about GenAI in higher education: not simply as a tool that weakens learning, but as a possible pedagogical partner when students use it strategically and critically. I especially appreciate how the article distinguishes between passive over-reliance and purposeful cognitive offloading, where AI can free up mental space for deeper reflection, synthesis, and transformative learning while still requiring students to remain vigilant, agentive, and intellectually engaged.

    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.

    About this Journal Article:

    A good reminder that research in Educational contexts are to be carefully considered.

    Recommended by Djuddah Leijen.

  • 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.

    About this Journal Article:

    This scoping review synthesizes 34 studies to identify three emerging assessment approaches (traditional, innovative/refocused, and GenAI-incorporated) and establishes “career-driven competencies” and “lifelong learning skills” as the new learning outcomes of the GenAI era. It provides the evidence-based foundation for moving from compliance-based assessment strategies to structural redesign that develops both human competency and human-AI collaboration—the dual priority at the heart of healthcare education’s assessment challenge.

    Recommended by E’lise Nissen.

  • 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.

    About this Journal Article:

    The paper explores students’ perceived benefits and challenges associated with the use of generative AI in translation practices. It also highlights the need for additional guidance from educators and institutions.

    Recommended by Nora Rivera.