Accepted for/Published in: JMIR Medical Education
Date Submitted: Aug 9, 2025
Date Accepted: Nov 6, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Implementing Large Language Models to Support Misconception-Based Collaborative Learning in Healthcare Education
ABSTRACT
This paper proposes a framework for leveraging large language models (LLMs) to generate misconceptions as a tool for collaborative learning in healthcare education. While misconceptions—particularly those generated by AI—are often viewed as detrimental to learning, we present an alternative perspective: that LLM-generated misconceptions, when addressed through structured peer discussion, can promote conceptual change and critical thinking. The paper outlines practical use cases across healthcare disciplines, including both clinical and basic science contexts. It also highlights the need for medium- to long-term research to evaluate the impact of LLM-supported learning on student outcomes. This framework may support healthcare educators globally in integrating emerging AI technologies into their teaching, regardless of disciplinary focus.
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Copyright
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