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TRV-2026-1044Certified recordPeer-reviewed

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

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 Th…

Health · The Trace — both readings · certified 2026-09-10 · v1 · article view · machine-readable

Current reading — gain

Undergraduate medical students who used LLM-powered virtual standardized patients as extracurricular self-practice achieved higher end-of-term history-taking performance at an OSCE with real standardized patients compared to routine instruction.

Current reading — problem

Students with medium and low baseline history-taking proficiency showed relatively limited score improvements from LLM-VSP self-practice, with practice frequency alone not independently predicting final performance.

What this doesn’t fix

Effect may depend on baseline proficiency, with preliminary evidence of a cognitive threshold where only students with solid theoretical foundations benefit substantially, and assignment was based on voluntary participation requiring propensity matching rather than randomization.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-1044, v1: “Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study.” Truvace, 2026-09-10. /record/TRV-2026-1044 (accessed at citation time). sha256 75073b2f5a6fb37c…

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