University Chemistry ›› 2025, Vol. 40 ›› Issue (9): 253-263.doi: 10.12461/PKU.DXHX202503020

Special Issue:

• Special Subject • Previous Articles     Next Articles

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

Daxin Liang, Yudong Li, Haiyue Yang, Bailing Chen, Zhiming Liu, Chengyu Wang   

  1. College of Materials Science and Engineering, Northeast Forestry University, Harbin 150040, China
  • Received:2025-03-03 Accepted:2025-05-13 Published:2025-09-16
  • Contact: Chengyu Wang E-mail:wangcy@nefu.edu.cn

Abstract: 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.

Key words: AI empowerment, Materials chemistry, “Learning-understanding-application” model, Intelligent assessment