Effect of Large Language Model–Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study

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BACKGROUND: Medical history taking (MHT) is a foundational clinical competency for medical students; however, traditional training models using standardized patients face challenges such as resource constraints. Large language model-powered virtual standardized patients (LLM-VSPs) offer a safe, repeatable platform for self-directed practice with AI-automated feedback. Nevertheless, their effectiveness in authentic teaching environments and underlying learning mechanisms require further investigation. OBJECTIVE: This study aims to evaluate the impact of an LLM-VSP system as an extracurricular self-practice tool on undergraduate medical students' MHT performance in an authentic educational setting without disrupting routine instruction, and to further explore potential associations among practice behaviors, baseline proficiency, and intervention effects. METHODS: This prospective cohort study enrolled 168 third-year medical students. Based on voluntary participation, students were assigned to an intervention group (n=120, using LLM-VSP) or a control group (n=48, receiving routine instruction). Propensity score matching (PSM) balanced confounding factors, yielding 40 matched pairs. Baseline MHT performance was assessed via virtual patient examination after didactic instruction but before clinical practicum. The primary outcome was end-of-term MHT performance assessed at an Objective Structured Clinical Examination station with real standardized patients. The differences between groups were compared using independent samples t test, with robustness validated through multiple linear regression, sensitivity analyses, and Rosenbaum bounds analyses. Exploratory analyses investigated the association between practice behaviors and scores, and observed benefit differences across baseline levels. RESULTS: =0.708), with sensitivity analyses supporting reliability. Exploratory analysis suggested that mere practice behavior metrics were not independent predictors of final scores, potentially being constrained by baseline proficiency, implying a "cognitive threshold" for effective AI-assisted training. Subgroup analysis indicated a trend of differential benefits: high baseline students appeared to show larger gains (matched sample: mean difference 5.93, 95% CI 2.17-9.70; overall sample: mean difference 7.04, 95% CI 3.84-10.25), whereas improvements in medium and low baseline students were relatively limited (matched sample: mean difference 2.94-2.99; overall sample: mean difference 1.29-1.64). CONCLUSIONS: Introducing LLM-VSPs as a self-practice tool in diagnostics education may help improve undergraduate medical students' MHT performance. Preliminary evidence suggests a potential "cognitive threshold," implying students with solid theoretical foundations and higher baseline proficiency may better achieve skill transformation through AI-assisted autonomous practice. This provides a basis for future stratified teaching strategies and differentiated guidance for students of varying baseline levels.

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