ARTIFICIAL INTELLIGENCE-ASSISTED TEACHING MODEL AS A STRATEGY TO ENHANCE CORE COMPETENCIES OF CLINICAL MEDICINE UNDERGRADUATES: A SCIENTIFIC HYPOTHESIS.

Undergraduate clinical education is currently facing severe challenges, including the widening gap between theoretical knowledge and clinical practice, the explosive growth of evidence-based medical information, and the difficulty of traditional teaching models in providing personalized guidance and large-scale high-fidelity clinical scenario simulation. Conventional teaching methods, especially traditional Problem-Based Learning (PBL), are insufficient in real-time feedback, adaptive case generation, and immersive skill training. Artificial intelligence (AI) technologies represented by natural language processing (NLP), machine learning (ML), virtual reality (VR), and augmented reality (AR) provide a transformative solution. This paper proposes a scientific hypothesis: implementing a comprehensive AI-assisted teaching model integrating AI-generated personalized clinical cases, immersive simulation platforms, intelligent clinical reasoning support, real-time data-driven feedback, and AI-moderated collaborative PBL groups into the undergraduate curriculum can significantly improve the six core competencies of clinical medicine undergraduates, with significantly better effects than traditional PBL or lecture-based teaching. The mechanism lies in that the AI-assisted model activates personalized knowledge scaffolding, immersive skill practice, data-driven metacognition, and AI literacy development, which can more effectively solve the problems of insufficient clinical exposure, lack of personalized tutoring, and delayed feedback in traditional education. This hypothesis provides a complete theoretical framework, operational sub-hypotheses, and feasible testing strategies for the innovation and empirical research of AI-integrated medical education.