AI-Powered Robot Tutors Boost Medical Student Performance in Clinical Skills
A new study shows that AI-generated feedback from robotic virtual patients significantly improves how medical students perform during clinical assessments. The finding suggests healthcare institutions could scale personalized training at lower cost while standardizing education quality across programs.
Originaltitel: AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance: Nonrandomized Quasi-Experimental Study
BACKGROUND: Virtual patients (VPs) demonstrate effectiveness in improving clinical reasoning skills; however, traditional VP platforms often lack individualized feedback mechanisms. Advances in large language models (LLMs) enable automated analysis of student-VP interactions, providing scalable feedback on clinical performance. While artificial intelligence (AI)-enhanced social robotic VP platforms show promise for clinical reasoning training, no studies have examined whether AI-generated feedback integrated in such platforms improves clinical performance in standardized assessments. OBJECTIVE: This study evaluated whether AI-generated postconsultation feedback integrated into social robotic VP interactions improves medical students' clinical performance, emphasizing medical history taking and communication. METHODS: A quasi-experimental study with 115 sixth-semester medical students (N=157, 73.2% of eligible students) was conducted at Karolinska Institutet, Stockholm, Sweden, during spring 2025. Students were allocated by hospital site to receive (n=61, 53%) or not receive (n=54, 46.9%) AI-generated feedback following interactions with a Social AI-Enhanced Robotic Interface. All students completed 9 VP cases; the intervention group received approximately 1 page of structured feedback after each VP case. The feedback system used multiple LLMs following a 2-stage algorithm: assessing student-VP dialogues using an assessment rubric, then generating structured feedback on history-taking performance. Both groups participated in case-specific follow-up seminars led by consultant rheumatologists following each VP encounter. Clinical performance was assessed through an 8-minute objective structured clinical examination (OSCE)-based evaluation, with a standardized patient portraying axial spondylarthritis, evaluated by a blinded consultant rheumatologist using a 10-point rubric across 5 domains: communication at consultation start, generic medical history, targeted medical history, diagnostics and management reasoning, and communication at consultation end. RESULTS: Students receiving AI-generated feedback achieved significantly higher total OSCE scores (mean 7.39, SD 0.86 vs mean 6.68, SD 1.04 points; mean difference 0.70; 95% CI 0.35-1.06; P<.001; Cohen d=0.74). Domain-specific analysis revealed significant improvement in generic medical history after Bonferroni correction (mean 2.46, SD 0.65 vs mean 2.03, SD 0.79 points; P=.004; r=0.27), while other domains showed no significant differences: communication at start (P=.13; r=0.14), targeted medical history taking (P=.60; r=0.05), diagnostics and management (P=.14; r=0.14), and communication at consultation end (P=.31; r=0.09). Pass rates were significantly higher in the feedback group (96.7% vs 79.6%; odds ratio 7.55, 95% CI 1.51-72.2; P=.006), with a number needed to assess of 6 students, that is, for every 6 students receiving feedback, 1 additional student passed the assessment. CONCLUSIONS: AI-generated feedback following social robotic VP interactions significantly improved medical students' OSCE-based performance, particularly in generic medical history taking. These findings support integrating validated AI feedback systems as a supplement to expert-led teaching during VP simulations for clinical training and demonstrate the feasibility of scalable, automated feedback in medical education. The domain-specific improvements in generic medical history highlight the importance of targeted, competency-specific feedback design in VP platforms.