大学化学 >> 2025, Vol. 40 >> Issue (9): 253-263.doi: 10.12461/PKU.DXHX202503020

所属专题： AI赋能化学教育

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AI赋能材料化学专业“学-知-用”培养模式改革与实践

梁大鑫, 李煜东, 杨海月, 陈百灵, 刘志明, 王成毓   

  1. 东北林业大学材料科学与工程学院, 哈尔滨 150040
  • 收稿日期:2025-03-03 录用日期:2025-05-13 发布日期:2025-09-16
  • 通讯作者: 王成毓 E-mail:wangcy@nefu.edu.cn
  • 基金资助:
    黑龙江省高等教育学会高等教育研究课题“基于创新创业的教育链、创新链与产业链协同发展研究”(23GJYBC004)

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

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摘要： 在形成和发展新质生产力的背景下，人工智能(AI)已成为化学研究及教育教学领域的强大工具。本研究基于东北林业大学材料化学专业在产教融合背景下的“学-知-用”培养模式改革，结合AI技术，通过构建智能虚拟仿真实验平台和产教协同智能评估系统，有效解决了传统教育模式中理论实践脱节(学用转化率不足42%)、知识体系碎片化(跨课程知识关联度仅31.7%)、创新成果转化迟滞(平均转化周期超18个月)等突出问题。项目实施三年来取得显著成效：学生创新成果转化周期缩短至5.8个月，较传统模式提升67.8%；在中国国际大学生创新大赛等国家级竞赛中获奖数量增长147.8%。本研究验证了AI技术在破解产教融合深层矛盾中的独特价值，构建的“技术赋能-实践强化-产业反哺”教育生态体系，为新工科背景下材料化学专业人才培养提供了可复制范式。

关键词: AI赋能, 材料化学, “学-知-用”模式, 智能评估

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