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Citation: Daxin Liang, Yudong Li, Haiyue Yang, Bailing Chen, Zhiming Liu, Chengyu Wang. Reform and Practice of an AI-Empowered “Learning-Understanding-Application” Training Model in Materials Chemistry Education[J]. University Chemistry, ;2025, 40(9): 253-263. doi: 10.12461/PKU.DXHX202503020 shu

Reform and Practice of an AI-Empowered “Learning-Understanding-Application” Training Model in Materials Chemistry Education

College of Materials Science and Engineering, Northeast Forestry University, Harbin 150040, China

Corresponding author: Chengyu Wang, wangcy@nefu.edu.cn
Received Date: 3 March 2025
Accepted Date: 13 May 2025

In the context of developing new quality productive forces, artificial intelligence (AI) has become a powerful tool in chemical research and education. This study presents the reform of the “Learning-Understanding-Application” cultivation model in Materials Chemistry at Northeast Forestry University, integrating AI technologies through the establishment of an intelligent virtual simulation platform and an industry-education collaborative intelligent evaluation system. These innovations effectively address key challenges in traditional education: the theory-practice disconnect (with knowledge application rates below 42%), fragmented knowledge systems (exhibiting only 31.7% cross-course relevance), and delayed innovation commercialization (averaging over 18 months for implementation). Over three years of implementation, the project has demonstrated significant outcomes: the innovation-to-market cycle for student projects shortened to 5.8 months (a 67.8% improvement), while national competition awards (including those from the China International College Students’ Innovation Competition) increased by 147.8%. This research confirms the unique value of AI in resolving fundamental industry-education integration challenges and establishes a replicable “Technology Empowerment - Practice Reinforcement - Industry Feedback” educational ecosystem for materials chemistry education within the emerging engineering education framework.
- AI empowerment,
- Materials chemistry,
- “Learning-understanding-application” model,
- Intelligent assessment
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