ArticleJMIR medical education2026
Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMedical 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.
objectiveThis 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.
methodsThis 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.
resultsAfter PSM, baseline characteristics were balanced (standardized mean difference <0.1). The intervention group exhibited higher total MHT scores than controls (mean 87.71, SD 7.29 vs mean 83.74, SD 8.06; mean difference 3.98, 95% CI 0.55-7.40 points; P=.02; with a medium effect size of Cohen d=0.52). Advantages were observed in content (P=.03) and communication skills (P=.04) subscores. Regression analysis confirmed robust intervention effects (B=3.924, 95% CI 1.90-5.94; P<.001; R
conclusionsIntroducing 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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