ArticleJMIR medical education2026
User Acceptance of an AI-Powered Medical History-Taking Training System Among Undergraduate Medical Students: Mixed Methods 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
backgroundAI-powered virtual patient systems provide medical students with repeatable practice environments for history-taking training. However, user acceptance of such systems and the experience dimensions associated with that acceptance lack mixed methods evidence.
objectiveThis study aimed to (1) examine the associations of system experience and learning experience/intrinsic motivation with overall acceptance among undergraduate medical students using an AI-powered medical history-taking training and evaluation system (AMTES), (2) explore user experience patterns through open-ended questions, and (3) integrate quantitative and qualitative findings to inform system refinement and pedagogical implementation.
methodsA cross-sectional convergent mixed methods design was used. A total of 66 undergraduate medical students at a Chinese medical college completed a postuse questionnaire after AMTES training. The primary outcome was an overall acceptance composite combining use intention, recommendation intention, and overall satisfaction. Associations with system experience and learning experience/intrinsic motivation were examined using linear regression with heteroscedasticity-consistent SEs (type 3) and bootstrap CIs. Sensitivity analyses included covariate adjustment, single-outcome models, a fractional logit model, and content-overlap sensitivity checks for the system-experience composite. Open-ended responses were analyzed using codebook-oriented thematic analysis and integrated with quantitative findings through a joint display.
resultsBoth system experience and learning experience/intrinsic motivation were positively associated with overall acceptance (standardized β=.526; P<.001 and standardized β=.377; P=.002, respectively; R
conclusionsIn this exploratory cohort of undergraduate medical students, both system interaction quality and perceived learning value were positively associated with overall acceptance of AMTES, with system interaction quality showing the stronger association within the study's measurement specification. Dialogue coherence, semantic understanding, and scoring-feedback alignment emerged as the most frequently nominated refinement priorities and are plausible candidate targets for improving acceptance-related perceptions. This study emphasizes implementation-level acceptance rather than solely technical reliability or educational effectiveness. Interaction-quality problems may be associated with less favorable acceptance even when learning value is recognized. These findings may inform system refinement priorities and the curricular integration of AI-powered history-taking training systems, while larger multicenter and longitudinal studies are needed to examine whether improvements in interaction quality translate into gains in learner acceptance and downstream training outcomes.
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