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Friday May 29, 2026 10:30am - 11:20am EDT
This session explores the potential of generative AI to dramatically accelerate the development of Open Educational Resources (OER). OER are teaching materials that can be retained, reused, revised, remixed, and redistributed due to their license. During the 2021-2022 academic year, college students spent up to $1,240 per year on books; this cost led 63 percent of students to decide not to purchase a textbook. OER may help reduce this financial barrier by providing access to free or low-cost instructional materials. Additionally, the use of OER allows instructors to customize course materials, better aligning them with learning objectives.


OER are in the public domain; therefore, instructors can use tools like generative AI to assist in many tasks relating to their creation. This presentation will introduce and demonstrate the "AI Sandwich" framework for OER creation using AI. The “AI Sandwich” framework has the instructor provide the initial outline, the AI generates a draft, followed by the instructor reviewing and editing the content.


I will showcase the successful application of this technique at Northern Michigan University in the creation of multiple OER textbooks, including Personal Wellbeing, Health Information Literacy, and ECG Interpretation. This practical demonstration will illustrate how the "AI Sandwich" framework can streamline the process, enabling instructors to produce high-quality, customized course materials that are better aligned with specific learning objectives, ultimately improving equitable access and student outcomes.
Speakers
avatar for Matt Kilgas

Matt Kilgas

Associate Professor, Northern Michigan University
Dr. Matthew Kilgas is an Associate Professor in the School of Health and Human Performance at Northern Michigan University (NMU) and the 2025–2027 NMU Teaching and Learning Scholar. His research focuses on exercise-based interventions to restore musculoskeletal function and improve... Read More →
Friday May 29, 2026 10:30am - 11:20am EDT
Peninsula I

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