ChatGPT versus human authors: A comparative study of concept maps for clinical reasoning training with virtual patients

Abstract

PURPOSE: This study investigates whether ChatGPT can generate clinically accurate and pedagogically valuable maps for clinical reasoning (CR) training. The aim is to assess its potential as a tool for supporting the creation of high-quality educational resources for CR training. MATERIALS AND METHODS: -tests and interrater reliability using weighted Cohen's kappa. RESULTS: < 0.001), though graph density did not differ significantly. Clinician evaluations showed comparable clinical content quality across both groups, with no statistically significant differences in concept or connection ratings. The educational review revealed that while ChatGPT maps offered comprehensive information, they lacked abstraction, prioritization, and contextual alignment, occasionally exceeding the optimal cognitive load for learners. CONCLUSIONS: ChatGPT can reliably generate concept maps that match expert-level clinical accuracy. However, limitations in educational clarity and usability underscore the need for expert refinement. With appropriate oversight, large language models (LLMs) such as ChatGPT can support efficient development of learning resources for CR education.

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