Work

Self-experienced empathetic behaviour patterns in medical students during virtual patient encounters: a comparison between an AI-enhanced social robot and a computer-based platform

Year: 2026

Type: article

Abstract: Objective To explore whether an AI-enhanced social robotic virtual patient (VP) platform reinforces empathetic behaviour patterns in medical students compared with a traditional computer-based platform. Methods Twenty-three sixth-semester medical students from Karolinska Institutet participated in semi-structured interviews following VP encounters with the Social AI-enhanced Robotic Interface (SARI) and, as a comparator, the computer-based Virtual Interactive Case system (VIC). Additionally, 178 students evaluated the VP platforms in empathetic training quantitatively using categorical nominal variables and a visual analogue scale (VAS), with a score of 0 indicating full preference for SARI and 10 full preference for VIC. Interview data were thematically analysed, and quantitative preferences were compared using the Fisher’s exact test with Monte Carlo simulation and the Wilcoxon signed-rank test. Results Thematic analysis yielded five major themes wherein students consistently reported that SARI facilitated greater empathetic engagement through multimodal interaction, ability to express emotions, and real-time communication adaptability. Quantitative analysis demonstrated a higher preference for SARI versus VIC (78% versus 6%; OR: 190.4; 95% CI: 76.8–472.0; p < 0.001), which remained consistent across subgroups of interest, i.e., female and male students, with and without prior experience in VPs, and students first exposed to SARI or first exposed to VIC. VAS data also showed a preference for SARI versus VIC (median: 2.00; IQR: 1.00–4.00; W : 738.5; r : 0.70; p < 0.001). Conclusion Our AI-enhanced social robotic VP platform was superior to a traditional computer-based VP platform in fostering empathetic engagement in medical students through enhanced authenticity and interactivity, supporting its potential to supplement clinical rotations.

Source: Frontiers in Artificial Intelligence

DOI: 10.3389/frai.2026.1795842