What might it look like to treat generative AI as a partner in scholarship of teaching and learning? This episode showcases an open-access article by Janice Miller-Young, Lauren Hays, and Brett McCollum, published in Teaching & Learning Inquiry.
See our full episode notes at https://www.centerforengagedlearning.org/exploring-genais-potential-as-a-sotl-partner/.
What might it look like to treat generative AI as a partner in scholarship of teaching and learning? This episode features an open-access article that examines how SoTL scholars can integrate large language models—tools like ChatGPT, Claude, and Copilot—into research in ways that strengthen, rather than erode, scholarly integrity:
Miller-Young, Janice, Lauren Hays, and Brett McCollum. 2026. “Exploring GenAI’s Potential Contributions as a Partner in SoTL Research: The Practice Model.” Teaching & Learning Inquiry 14: 1-19. https://doi.org/10.20343/teachlearninqu.14.7
This episode was hosted, edited, and produced by Jessie L. Moore, Director of the Center for Engaged Learning and Professor of Professional Writing & Rhetoric.
60-Second SoTL is produced by the Center for Engaged Learning at Elon University.
Music: “Cryptic” by AudioCoffee.
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Jessie L. Moore:
What might it look like to treat generative AI as a partner in scholarship of teaching and learning? That’s the focus of this week’s 60-second SoTL from Elon University’s Center for Engaged Learning. I’m Jessie Moore.
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In “Exploring GenAI’s Potential Contributions as a Partner in SoTL Research: The PRACTICE Model,” Janice Miller-Young, Lauren Hays, and Brett McCollum explore how SoTL scholars can integrate large language models—tools like ChatGPT, Claude, and Copilot—into research in ways that strengthen, rather than erode, scholarly integrity. Their article appears in Teaching & Learning Inquiry, an open-access journal.
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The authors start with a tension many of us feel: GenAI can be useful for brainstorming, organizing ideas, and even expanding a methodological toolkit—but it can also introduce real risks: unreliable outputs, embedded bias, hallucinated citations, and ethical concerns like transparency and student privacy.
Rather than focusing on GenAI’s outputs, the authors decided to focus on two questions related to exploring GenAI’s potential as a collaborative tool:
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Miller-Young, Hays, and McCollum designed a human-AI partnership approach, posing the same prompts to three platforms—Claude, ChatGPT-4, and Copilot. First, they asked these GenAI platforms to outline a manuscript guided by their questions. Second, they asked the platforms to respond to key questions, like: How does GenAI define SoTL? Can GenAI reveal new knowledge in SoTL? And can GenAI be a collaborative research partner in SoTL? Third, as the authors engaged in drafting and revising full drafts of each section of their manuscript, they continued to work with ChatGPT and Claude as conversational partners and asked the GenAI tools for feedback.
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Their analysis is… both encouraging and cautionary.
On the encouraging side: the tools can often capture the broad contours of SoTL, help synthesize ideas, and sometimes surface connections outside a researcher’s usual reading pathways. In other words, GenAI can be a catalyst for thinking—especially when a scholar uses it as an “interlocutor,” prompting it to offer counterarguments, critique a paragraph, or suggest alternative framings.
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But the limitations are clear—and important. The models sometimes misattribute claims, generate citations that don’t hold up, and tend to reproduce dominant, aspirational definitions of SoTL while skipping the debates—like questions about inclusivity, power, and what counts as evidence. The authors also note repetitiveness and an overreliance on older sources, which can quietly reinforce the biases already embedded in what the models were trained on.
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Side note: As a journal editor, those limitations are apparent when authors try to use GenAI to quickly respond to requests to engage with more SoTL literature. While Felten (2013) is a great foundational resource on guiding principles for SoTL, a thorough engagement with SoTL scholarship necessitates grappling with other, more recent publications, including scholarship from Latin SoTL, the global south, and other regions.
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Miller-Young, Hays, and McCollum also grapple with the ethical concerns of collaborating with GenAI. One major concern is data governance: SoTL often involves student reflections, assignments, or classroom interactions—so uploading student-generated data into a cloud-based tool can create serious privacy and consent issues. The authors emphasize the need to follow institutional policy, consult ethics review processes, anonymize carefully, and avoid putting identifiable student data into external systems.
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All of this leads to their PRACTICE model—eight recommendations for using GenAI in SoTL research responsibly:
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I appreciate their bottom line: GenAI’s greatest value in SoTL isn’t efficiency. It’s dialogue—a way to test ideas, surface assumptions, and sharpen thinking—if we engage with it critically, reflexively, and ethically.
And one final side note as an editor: Always check your target journal’s AI policies before you engage GenAI as a partner. Teaching & Learning Inquiry, for example,offers generative AI guidance for authors and reviewers, centering ethical use, transparency, and author responsibility for accuracy and academic integrity.
To learn more about this study and the PRACTICE model, visit our show notes for a link to the open access article.
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Jessie Moore:
Join us for our next episode of 60-second SoTL from Elon University’s Center for Engaged Learning for another snapshot of recent scholarship of teaching and learning. Learn more about the Center at www.CenterForEngagedLearning.org.
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