Observational studyJournal of medical Internet research2025
AI-Enhanced Social Robotic Versus Computer-Based Virtual Patients for Clinical Reasoning Training in Medical Education: Observational Crossover Cohort Study.
Observational study in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Trial
- Article
- AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance: Nonrandomized Quasi-Experimental Study.JMIR medical education · 2026Article
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVirtual patient (VP) simulations can be used to practice clinical reasoning (CR) in controlled learning environments. Traditional computer-based VP platforms often lack the authenticity and interactivity required for effective CR training. Artificial intelligence (AI)-enhanced social robotic VPs can enhance realism and engagement; however, quantitative evidence comparing them with conventional VP platforms remains limited.
objectiveWe compared medical students' experience of an AI-enhanced social robotic versus a conventional computer-based VP platform regarding the extent to which the design characteristics of the respective platform facilitate CR skill training.
methodsThis observational crossover cohort study involved 178 sixth-semester medical students at Karolinska Institutet, Stockholm, Sweden (response rate: 42.3%; 178 of 421 invited students; Spring 2024-Spring 2025), who experienced both a large language model-enhanced social robotic VP platform supporting dialogue (social artificial intelligence-enhanced robotic interface [SARI]) and a conventional computer-based VP platform (virtual interactive case [VIC]) during their clinical rotation within rheumatology. Platform order was determined by clinical rotation scheduling. VP design was evaluated using a validated questionnaire across 5 domains: authenticity, professional approach, coaching quality, learning effects, and overall judgment. Students' CR training preferences were assessed using categorical responses and a Visual Analogue Scale, where a lower score favored SARI and a score of 5 indicated equal preference between platforms.
resultsSARI outperformed VIC across all 5 VP design domains. Students rated SARI higher for authenticity (median 4.0, IQR 3.5-4.5 vs 3.0, IQR 2.5-3.5; P<.001), professional approach (median 4.5, IQR 4.0-4.8 vs 4.0, IQR 3.5-4.5; P<.001), coaching quality (median 4.3, IQR 4.0-4.7 vs 4.0, IQR 3.7-4.7; P<.001), learning effect (median 4.4, IQR 4.0-5.0 vs 4.0, IQR 3.5-4.5; P<.001), and overall judgment (median 5.0, 4.0-5.0 vs 4.0, IQR 4.0-5.0; P<.001). Students strongly preferred SARI for CR training (72% vs 14%; odds ratio [OR] 27.1, 95% CI 14.3-53.7; P<.001), with Visual Analogue Scale scores confirming this preference (median 3.0, IQR 2.0-5.0; P<.001). Preferences were consistent across most subgroups (sex, prior VP experience, and platform order); in 2 subgroups, the difference was not significant, that is, students with prior VP experience (62% vs 38%; OR 2.6; 95% CI 0.8-8.9; P=.11) and students first introduced to VIC (55% vs 45%; OR 1.5; 95% CI 0.7-2.9; P=.33).
conclusionsOur findings provide the first quantitative evidence that AI-enhanced social robotic VPs offer superior design characteristics than conventional computer-based platforms for CR training in medical education. These results support the use of AI-driven social robots for VP simulations to better prepare medical students for real clinical encounters, and warrant future research on objective CR skill outcomes and long-term transfer to clinical practice. Unlike previous qualitative studies examining each platform separately, this study provides the first quantitative comparison of design characteristics between AI-enhanced social robotic and conventional computer-based VPs.
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