Implementing LLMs 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.

Implications for AI in Education

The paper extends the refutation-text/misconception literature by using AI as the generator of misconceptions (and implicit refutation targets) for collaborative learning, rather than only as the corrector. For practice in health care education, it offers a concrete, scalable method for misconception-based learning that does not depend on educators' manual generation of misconceptions. It connects to the knowledge base's Collaborative Learning, Misconceptions, conceptual change, and Generative AI concepts.

Connected Concepts

• Refutation Text
• Misconceptions
• Collaborative Learning
• Medical Education
• Critical Thinking

Connected Articles

• AI Tutors Vs Tenacious Myths Personalised Dialogue 2026 — Personalised AI dialogue for misconception correction
• Akdogan Heat Temperature Conceptual Change Thesis 2025 — Expert/AI conceptual change text vs. AI dialogue

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.