Abstract: Addressing the persistent challenges in medical university organic chemistry instruction—namely, the abstract and conceptually demanding nature of the subject, low student engagement, and a lack of personalized support—this paper develops and implements an AI (Artificial intelligence)-assisted teaching model grounded in the SMART-C framework. Guided by design-based research methodology, the model integrates six core components: Student-centered learning (S), Molecular visualization (M), AI assistance (A), Reflective thinking (R), Team collaboration (T), and Continuous assessment (C). Utilizing the open-source XGBoost learning analytics model, augmented reality (AR) molecular simulations, standardized seminar formats, and reflective evaluation tools, the model establishes a six-stage progressive learning pathway encompassing diagnosis, planning, implementation, collaboration, reflection, and assessment. Validated through a full-semester curriculum intervention at our institution, the model has demonstrated its capacity to progressively enhance students’ conceptual mastery of organic chemistry, learning engagement, and teamwork skills. These findings provide a replicable, phased reference for localized instructional reform in organic chemistry at comparable medical colleges and universities.

Key words: SMART-C framework, Medical university, Organic chemistry, AI-assisted, Instructional innovation