Implementing Large Language Models to Support Misconception-Based Collaborative Learning in Health Care Education

TL;DR

This paper suggests using AI to create misconceptions in healthcare education to encourage discussion and deeper learning among students.

Contribution

The novel contribution is using AI-generated misconceptions as a deliberate educational tool to foster collaborative learning and critical thinking.

Findings

LLM-generated misconceptions can be used to promote conceptual change when discussed in structured peer settings.

A 10-step practical framework is proposed for educators to implement this approach across healthcare disciplines.

The paper emphasizes the need for further research on the long-term impact of AI-supported learning in education.

Abstract

This paper proposes a framework for leveraging large language models (LLMs) to generate misconceptions as a tool for collaborative learning in health care 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 use cases across health care disciplines, including both clinical and basic science contexts, and a practical 10-step guidance for educators to implement the framework. 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 health care educators globally in integrating emerging AI technologies into their teaching, regardless of the disciplinary focus.

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