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Citation: Zijun Huang, Feng Wu, Shaofeng Pi, Saijin Huang, Zhengjun Fang. Knowledge Graph-based Development of AI Curriculum for Inorganic Chemistry Experiments and Exploration of New Teaching Paradigm[J]. University Chemistry, ;2025, 40(9): 228-237. doi: 10.12461/PKU.DXHX202504052 shu

Knowledge Graph-based Development of AI Curriculum for Inorganic Chemistry Experiments and Exploration of New Teaching Paradigm

College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, Hunan Province, China

Corresponding author: Zijun Huang, Zhengjun Fang,
Received Date: 15 April 2025
Accepted Date: 26 June 2025

In the context of educational digital transformation, this study addresses the challenges of fragmented knowledge, low resource integration, and insufficient personalized support in traditional inorganic chemistry experiment teaching. A “Knowledge Graph + AI” integrated model is proposed, which constructs a “concept-operation-resource” triple network to achieve structured mapping of essential elements such as experimental principles and operational standards. Through an intelligent tutoring system and dynamic reasoning algorithms, the model supports personalized learning path planning and formative assessment. Teaching practice demonstrates that this approach significantly enhances students’ knowledge integration efficiency and innovation capabilities, providing a novel pathway for the digital transformation of experimental education.
- Knowledge graph,
- Inorganic chemistry experiments,
- Experimental teaching reform,
- Intelligent tutoring system
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