Agentic education · Learning science

Actionable companion · Retention & transfer

From AI evidence to responsible practice: AI-Enhanced Social Robotic Versus Computer-Based Virtual Patients for Clinical Reasoning Training in Medical Education: Observational Crossover Cohort Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions education findings do not establish effectiveness in K–12 classrooms.

Original authors’ abstract, reproduced under the linked source license:

Background Virtual 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. Objective We 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. Methods This 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. Results SARI 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). Conclusions Our 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.

What kind of evidence is this?

Embodied learning agents · Qualitative / mixed methods

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

We conducted an observational crossover cohort study to compare VP design elements that support CR skill training in medical education in an LLM-empowered social robotic platform, that is, an in-house developed social artificial intelligence–enhanced robotic interface (SARI) [20-22], and a computer-based VP platform, that is, the virtual interactive case system (VIC) [28]. This study reports according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) [29] guidelines for observational studies and the GREET (Guideline for Reporting Evidence-Based Practice Educational Interventions and Teaching) guidelines [30] for educational references.

Limitations and uncertainty · source passage

Several limitations warrant consideration when interpreting our results. First, our findings are based on students’ perceptions of VP design elements rather than objective measures of CR skill acquisition. Moreover, the internal consistency of the adapted questionnaire was not assessed in our study population. Future research that incorporates validated CR assessments and longitudinal evaluation of skill transfer to real clinical encounters is warranted [42]. While students consistently favored SARI, it is important to acknowledge that positive reception of novel technology does not always correlate with improved educational outcomes [43]. Novelty effects may have influenced students, as this was the first exposure to SARI for most participants, whereas computer-based platforms might already have been familiar to some.

Design the system before the pilot

Compare the embodied interaction with an equivalent human-led or screen-based activity. Separate the effects of novelty and social presence from learning. Review physical access, sensory needs and adult supervision.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Alexander Borg; Jonathan Schiött; William Ivegren; Cidem Gentline; Viking Huss; Anna Hugelius; Benjamin Jobs; Fabricio Espinosa; Mini Ruiz; Samuel Edelbring; Carina Georg; Gabriel Skantze; Ioannis Parodis. AI-Enhanced Social Robotic Versus Computer-Based Virtual Patients for Clinical Reasoning Training in Medical Education: Observational Crossover Cohort Study. Journal of Medical Internet Research (2025-11-27). https://doi.org/10.2196/82541

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.