Implementing Large Language Models to Support Misconception-Based Collaborative Learning in Healthcare Education (Preprint)

<sec> <title>UNSTRUCTURED</title> 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. </sec>