大学化学 >> 2025, Vol. 40 >> Issue (9): 206-219.doi: 10.12461/PKU.DXHX202503069

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生成式人工智能驱动化学创新教学的现状及展望

吴凌莉1, 雷圣宾2,3   

  1. 1 西北民族大学医学部, 兰州 730000;
    2 天津大学理学院化学系, 有机集成电路教育部重点实验室, 天津市分子光电科学重点实验室, 天津 300072;
    3 兰州交通大学化学与化工学院, 兰州 730070
  • 收稿日期:2025-03-18 录用日期:2025-05-16 发布日期:2025-09-16
  • 通讯作者: 吴凌莉, 雷圣宾 E-mail:wulingli19831106@163.com;shengbin.lei@tju.edu.cn
  • 基金资助:
    甘肃省自然科学基金(24JRRA57)

Generative AI-Driven Innovative Chemistry Teaching: Current Status and Future Prospects

Lingli Wu1, Shengbin Lei2,3   

  1. 1 Medical College, Northwest Minzu University, Lanzhou 730000, China;
    2 Department of Chemistry, School of Science, Tianjin University & Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Science, Tianjin 300072, China;
    3 School of Chemistry and Chemical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Received:2025-03-18 Accepted:2025-05-16 Published:2025-09-16
  • Contact: Lingli Wu, Shengbin Lei E-mail:wulingli19831106@163.com;shengbin.lei@tju.edu.cn

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摘要： 化学是化工、材料、环境、生物、能源及药学等多个专业的基础课程，生成式人工智能(Generative AI，GAI)的快速发展正在深刻改变化学教育的实践模式。本综述旨在聚焦生成式人工智能驱动化学创新教学这一领域，通过分析现状探寻其发展趋势。首先从教学设计和课程建设、个性化学习路径设计、教学方法改革三大维度，系统回顾了GAI在化学教学中的应用现状。随后，从多模态可视化教学融合以及师生个性化学习的教学实践路径两大方面深入探讨了GAI在化学教学中的创新方法。最后，我们客观分析了GAI驱动化学创新教学研究中的关键挑战并详细讨论了未来的发展方向，为GAI在化学创新教学领域的深入应用提供参考。

关键词: 人工智能, 生成式人工智能, 化学教学, 教学创新

Abstract: Chemistry serves as a foundational discipline across multiple fields, including chemical engineering, materials science, environmental science, biology, energy, and pharmaceutical sciences. The rapid advancement of generative artificial intelligence (Generative AI, GAI) is significantly transforming the paradigm of chemistry education. This review focuses on the innovative application of GAI in chemistry teaching. We systematically examine the current status of GAI in chemistry education through three key dimensions: instructional design and curriculum development, personalized learning path design, and teaching method reform. Furthermore, we explore innovative approaches of GAI in chemistry teaching, particularly in the integration of multimodal visualization teaching and the implementation of personalized learning strategies for both teachers and students. Finally, we critically analyze the key challenges in GAI-driven innovative chemistry teaching and provide a detailed discussion on future directions, offering valuable insights for the deeper integration of GAI in chemistry education.

Key words: Artificial intelligence, Generative artificial intelligence, Chemistry teaching, Pedagogical innovation