Paradigm Reconstruction and Practical Pathways of AI-Enabled Physical Chemistry Teaching

Physical chemistry is a core course for chemistry‑related majors. Its teaching faces problems such as abstract concepts, complicated mathematical deductions, and disconnection between theory and practice, making it difficult to improve teaching quality. Generative artificial intelligence offers new solutions to these teaching difficulties. This paper proposes strategies to reduce cognitive barriers, reshape classroom models, and optimize evaluations. Besides, it also puts forward risk prevention measures for “cognitive outsourcing” and “AI hallucinations”. This research holds that the essence of AI-enabled physical chemistry teaching lies in realizing the paradigm shift from unified content supply to the construction of an educational ecosystem through technology, and its effectiveness depends on consistently prioritizing the cultivation of students’ higher‑order thinking abilities.