Enhancing Knowledge Integration and Self-Directed Learning in Undergraduate Medical Education Through an AI-Based...

To evaluate the efficacy of the Sun Yat-sen Intelligent Education Platform-an AI-powered, multimodal learning system-in bridging the gap between preclinical and clinical knowledge and enhancing learning outcomes among undergraduate medical students.

A mixed-methods, prospective, randomized controlled trial was conducted with 80 fourth-year medical students at Sun Yat-sen University. Participants were randomly assigned to either an intervention group (n = 40), which used the AI platform alongside traditional teaching, or a control group (n = 40), which received standard instruction only. The study spanned six weeks, culminating in an Objective Structured Clinical Examination (OSCE). The primary outcome was the OSCE composite score. Secondary outcomes included subdomain scores (history taking, differential diagnosis, treatment planning) and student-reported satisfaction via a validated anonymous survey using 5-point Likert scales and open-ended questions assessing usability, educational value, and perceived integration of knowledge.

The intervention group achieved significantly higher OSCE total scores than the control group (85.7 ± 5.8 vs. 78.3 ± 7.1; p < 0.001). Superior performance was observed in systematic history taking (p = 0.008), comprehensiveness of differential diagnosis (p < 0.001), and evidence-based treatment justification (p = 0.002). Over 90% of students reported that the platform effectively linked foundational and clinical knowledge, with high ratings for usability and educational support.

These findings suggest that the AI-based multimodal platform, The Sun Yat-sen Intelligent Education Platform, may support knowledge integration and self-directed learning in undergraduate medical education, though further validation in larger, multi-institutional settings is warranted.