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Citation: Ruming Yuan, Laiying Zhang, Xiaoming Xu, Pingping Wu, Gang Fu. Generative Artificial Intelligence Empowering Physical Chemistry Teaching[J]. University Chemistry, ;2025, 40(9): 238-244. doi: 10.12461/PKU.DXHX202504069 shu

Generative Artificial Intelligence Empowering Physical Chemistry Teaching

National Demonstration Center for Experimental Chemistry Education (Xiamen University), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian Province, China

Corresponding author: Ruming Yuan, yuanrm@xmu.edu.cn
Received Date: 28 April 2025
Accepted Date: 19 June 2025

The groundbreaking advancements in Generative Artificial Intelligence (GAI) technology have introduced novel perspectives for reforming traditional educational curricula. This study focuses on physical chemistry courses, presenting and implementing an AI-enhanced teaching reform strategy. By harnessing DeepSeek’s capabilities in natural language understanding and reasoning, we have developed an integrated approach incorporating Xmind, Mathematica, and JiMing AI. This integration facilitates the creation of a systematic, hierarchical mind map that enables dynamic knowledge connections and intelligent expansion. Furthermore, complex mathematical models are transformed into dynamic, interactive representations, effectively overcoming the challenges posed by abstract mathematical concepts. Additionally, key concepts are presented through high-precision diagrams and concise video demonstrations, achieving a “visual, interactive, and mobile” delivery of educational content. This approach provides a practical framework for the digital transformation of chemistry education.
- DeepSeek,
- Systematic hierarchical mind map,
- Interactivity
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