March 3, 2026Open Access

Meeting Accreditation on a Deadline: Using Generative AI to Rapidly Develop a Graduate Course

Key Points

Course materials were efficiently generated using large language models, enhancing instructor productivity and course effectiveness.
The process involved an 'expert-in-the-loop' framework, combining AI-generated content with faculty curation to ensure quality.
A focus on human-AI collaboration allowed for active learning experiences that were more impactful for students during course delivery.
This innovative approach to accreditation under tight deadlines may enable future advancements in educational methodologies.

Abstract

This entry details an "expert-in-the-loop" framework for rapidly developing an accredited, graduate-level Health Physics course under significant time constraints. The strategy utilized Large Language Models to generate foundational course materials including the syllabus, lecture outlines, and in-class simulations, which were then rigorously curated by faculty. This human-AI collaborative approach enhanced efficiency, enabling instructors to focus more on high-impact, active learning experiences for students.

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Authors

Cory Knill

Sean P. Devan

Journals

Journal of International Crisis and Risk Communication Research

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Meeting Accreditation on a Deadline: Using Generative AI to Rapidly Develop a Graduate Course

Key Points

Abstract

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Discussion

Authors

Journals

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References and Citations

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