Abstract: The rapid advancement of generative artificial intelligence (GenAI) is reshaping the landscape of higher education. As a discipline characterized by strong experimental rigor, conceptual abstraction, and a highly structured knowledge system, organic chemistry faces both opportunities and challenges in the era of AI integration. On the one hand, models such as ChatGPT and DeepSeek provide students with instant knowledge generation, support for understanding reaction mechanisms and core concepts, and assistance with academic writing—thereby enhancing learning motivation, fostering self-explanation, and promoting knowledge transfer. On the other hand, unguided or uncritical use may lead to overreliance, weaken students’ critical thinking, and create risks to academic integrity. Guided by an educational philosophy of critical optimism, this paper examines how GenAI redefines teacher-student dynamics within AI-mediated learning environments, focusing on the reconstruction of student roles, the transformation of teacher responsibilities, and the enhancement of transferability in learning. Building on this analysis, we propose a pedagogical framework centered on AI literacy, prompt design, and transfer-oriented learning tasks, and elaborate its implementation and evaluation through classroom practice. The framework offers a scalable reference model for instructional innovation and educational assessment in undergraduate organic chemistry.

Key words: Generative artificial intelligence, Organic chemistry education, Learning transfer, Student roles, Teacher responsibilities, Teaching innovation