Artificial intelligence (AI) is increasingly being integrated into knowledge acquisition, skills training, and competency assessment in medical education, prompting changes in training models and governance mechanisms. To examine the progress and implementation challenges of AI-driven paradigm reshaping in medical education, this study combines literature analysis with engineering-oriented reasoning to review applications, challenges, and practices in China and abroad from the perspective of students' learning experiences and to propose response pathways for China. The analysis suggests that AI has expanded from assisting individual tasks to supporting teaching and assessment processes, with teachers becoming learning facilitators and designers of human‒AI collaborative teaching, and students becoming active learners. The implementation of AI in medical education faces three mismatches: technological systems versus learning capabilities, algorithmic objectives versus educational goals, and efficiency versus humanistic values. These mismatches are manifested as cognitive overload, goal substitution and automation bias, as well as erosion of student data sovereignty and insufficient humanistic education, respectively. Based on an analysis of coordination across technological, organizational, and governance levels, a three-dimensional pathway encompassing capability building, system optimization, and governance improvement is proposed. It includes integrating digital literacy and human‒AI collaboration training at successive stages, improving the controllability, explainability, and reviewability of AI teaching platforms, and strengthening data protection, ethical standards, and responsibility allocation. The integration of AI into medical education should be guided by medical talent training goals and supported by the coordination among technology, educational organization, and governance, thus to cultivate medical professionals with digital literacy and humanistic qualities.