引用本文:
袁汝明, 张来英, 徐晓明, 吴平平, 傅钢. 生成式人工智能助力物理化学教学[J]. 大学化学,
2025, 40(9): 238-244.
doi:
10.12461/PKU.DXHX202504069
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
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
生成式人工智能助力物理化学教学
English
Generative Artificial Intelligence Empowering Physical Chemistry Teaching
Abstract:
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.
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Key words:
- DeepSeek
- / Systematic hierarchical mind map
- / Interactivity
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