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

Brandon C J Cheah^1,2, Shefaly Shorey³, Jun Hong Ch'ng¹

¹Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore, 117545, Singapore, 65 81503939.

JMIR Medical Education

|January 16, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) can generate health care education misconceptions to foster collaborative learning and critical thinking. This framework guides educators in using AI-generated misconceptions to enhance student conceptual change.

Keywords:

LLM collaborative learning health care education large language model misconception-based learning refutation text

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Area of Science:

Medical Education
Artificial Intelligence in Healthcare
Educational Technology

Background:

Misconceptions are often detrimental to learning.
Artificial intelligence (AI) and large language models (LLMs) are emerging technologies in education.
There is a need to explore novel pedagogical approaches using AI in healthcare education.

Purpose of the Study:

To propose a framework for using LLM-generated misconceptions in healthcare education.
To demonstrate how AI-generated misconceptions can be leveraged for collaborative learning.
To promote conceptual change and critical thinking in healthcare students.

Main Methods:

Developing a framework for LLM-generated misconception use.
Outlining use cases across clinical and basic science healthcare disciplines.
Providing a 10-step guidance for educators to implement the framework.

Main Results:

LLM-generated misconceptions, when used in structured peer discussion, can promote conceptual change.
The framework offers practical implementation guidance for educators.
Identified need for further research on long-term impacts of AI-supported learning.

Conclusions:

A novel framework supports healthcare educators in integrating AI technologies.
LLM-generated misconceptions can be a valuable tool for collaborative learning.
Further research is needed to evaluate the impact on student outcomes.