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

Synthesis: Cheah et al. (2026) propose a framework for leveraging large language models (LLMs) to generate misconceptions as a tool for misconception-based collaborative learning in health care education. While AI-generated misconceptions are often viewed as detrimental, the authors argue that LLM-generated misconceptions, when addressed through structured peer discussion, can promote conceptual change and critical thinking. The paper outlines use cases across clinical and basic-science health care disciplines, a practical 10-step guidance for educators, and calls for medium- to long-term research on LLM-supported learning outcomes. The framework positions LLM-generated misconception texts and refutation/discussion as a scalable alternative to educator-generated misconception-based learning.

Key Findings

• LLM-generated misconceptions as a pedagogical resource. The paper reframes AI-generated misconceptions from a problem to a tool: deliberately generated misconceptions, addressed through structured peer discussion and refutation, can drive conceptual change.
• Scaling misconception-based learning. Traditional misconception-based learning assumes stable, shared misconceptions across cohorts and depends on educator experience and time. LLMs can generate many, context-specific misconceptions cheaply, overcoming these scalability limits.
• A practical 10-step implementation framework. The authors offer step-by-step guidance for educators to use LLMs to generate and deploy misconception-based collaborative learning across health care disciplines (clinical and basic science).
• Critical thinking and misinformation literacy. The framework supports health care educators in cultivating students' capacity to detect and dismantle misinformation — a key competency given the harms of physician-spread misinformation.

What this means for practice

• Instructors. Generate misconception texts with an LLM instead of authoring them by hand: input lecture slides or textbook chapters, set the model's role (for example, a junior medical student), and deploy the output as the object of student refutation.
• Instructors. Review every generated misconception before class — LLMs such as ChatGPT-4o may occasionally provide misconceptions based on content errors rather than the intended conceptual target — and keep statements that isolate a single concept.
• Instructors. Run short group presentations in which students identify and explain each LLM-generated misconception, then have groups converse in a Socratic role-play to work through it.
• Instructors. Assess afterward by re-uploading anonymized student examination answers and lecture notes, using the comparison to judge whether the discussion changed students' understanding.
• Instructors. Teach students to flag absolute qualifiers such as "all" or "every" inside LLM-generated misconceptions, building the critical-thinking and misinformation-literacy skill the framework is designed to cultivate.

Limitations

• The paper is conceptual and reports no outcome data; the authors note that only 2 quantitative studies have been conducted on misconception-based reasoning in health care education, so the method's efficacy in this field is untested.
• More than half of the existing misconception-based learning studies come from other fields (Newtonian physics, genetically modified organisms, evolution); the authors caution that generalizing its efficacy to health care education may not be fully applicable.
• Implementation depends on an institutional pre-trained LLM chatbot and on uploading anonymized student examination answers and lecture notes, which adds data-governance requirements to the workflow.
• The authors flag LLM vulnerabilities as a key limitation of the pedagogical method, noting that adversarial-attack testing or encryption specific to LLM modalities has yet to be explored.

Citation

Cheah, B. C. J., Shorey, S., Ch'ng, J. H., & Tan, C. W. (2026). Implementing large language models to support misconception-based collaborative learning in health care education. JMIR Medical Education, 12, e81875.