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

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.