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AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance:

Alexander Borg¹, Jonathan Schiött¹, William Ivegren¹

¹Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, and Center for Molecular Medicine (CMM), Solna, Sweden.

JMIR Medical Education

|March 25, 2026

Summary

This summary is machine-generated.

AI-generated feedback significantly improved medical students' clinical performance in standardized assessments, particularly enhancing generic medical history taking skills. This scalable approach shows promise for supplementing virtual patient simulations in medical education.

Keywords:

AI artificial intelligence clinical reasoning educational technology large language models medical education social robotics virtual patients

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Area of Science:

Medical Education Technology
Artificial Intelligence in Healthcare
Clinical Reasoning Development

Background:

Traditional virtual patient (VP) platforms lack individualized feedback, hindering clinical reasoning skill development.
Large language models (LLMs) offer potential for automated analysis of student-VP interactions and scalable feedback.
AI-enhanced social robotic VP platforms are promising but require validation for improving clinical performance.

Purpose of the Study:

To evaluate if AI-generated feedback integrated with social robotic VP interactions enhances medical students' clinical performance.
To assess improvements in medical history taking and communication skills.

Main Methods:

A quasi-experimental study involved 115 medical students using a Social AI-Enhanced Robotic Interface for 9 VP cases.
The intervention group received AI-generated feedback post-interaction, while the control group did not.
Clinical performance was assessed via an objective structured clinical examination (OSCE) with blinded evaluation.

Main Results:

Students receiving AI feedback achieved significantly higher OSCE scores (mean difference 0.70, P<.001).
Significant improvements were noted in generic medical history taking (P=.004).
The feedback group demonstrated higher pass rates (96.7% vs 79.6%, P=.006).

Conclusions:

AI-generated feedback integrated into robotic VP interactions significantly boosts medical students' OSCE performance.
The findings support AI feedback systems as a valuable supplement to expert-led teaching in VP simulations.
Targeted feedback design is crucial for optimizing competency-specific improvements in medical training.