卢树华,田方,尹晓英

• 1:中国人民公安大学信息网络安全学院

摘要(Abstract)：

新一代人工智能(Artificial Intelligence, AI)背景下,大数据、智能涌现等新技术对教育教学产生重大影响。物理学是自然科学的基础,其学科思想和方法对人工智能的起源与发展产生重要推动作用。为更好把握前沿技术发展趋势,紧扣时代发展脉搏,本文以“人工智能+”课程思想为指导,探讨在大学物理教学理念、教学内容及教学模式等方面进行改革尝试;提出转变教学理念、积极拥抱人工智能,通过AI辅助教学提升人工智能素养,构建AI教学助手与课程知识图谱,体现教学时代性并深化教学内容的内涵与外延等改革创新举措,以期提升新时代大学物理及相关课程教学质量,实现教学相长。

关键词(KeyWords)： 大学物理;人工智能;教学改革

Abstract:

Keywords:

基金项目(Foundation): 中国人民公安大学双一流建设项目（2023jxyj57）

作者(Author): 卢树华,田方,尹晓英

DOI: 10.27024/j.wlygc.2025.07.16.03

参考文献(References)：

• [1] SAIF N,KHAN S U,SHAHEEN I,et al.Chat-GPT;validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism[J].Computers in Human Behavior,2024,154:108097.
• [2] 穆肃，陈孝然，周德青.生成式人工智能赋能教学设计分析：需求、方法和发展[J].开放教育研究，2025,31(1):61-72.MU S,CHEN X R,ZHOU D Q.Generative artificial intelligence empowers instructional design analysis:needs,methods,and prospects[J].Open Education Research,2025,31(1):61-72.(in Chinese)
• [3] TSAI M L,ONG C W,CHEN C L.Exploring the use of large language models (LLMs) in chemical engineering education:Building core course problem models with Chat-GPT[J].Education for Chemical Engineers,2023,44:71-95.
• [4] FUCHS K.Exploring the opportunities and challenges of NLP models in higher education:is Chat GPT a blessing or a curse?[C].Frontiers in Education Frontiers Media SA,2023,8:1166682.
• [5] HU B,ZHENG L,ZHU J,et al.Teaching Plan Generation and Evaluation With GPT-4:Unleashing the Potential of LLM in Instructional Design[J].IEEE Transactions on Learning Technologies,2024,17:1445-1459.
• [6] GOFMAN M,JIN Z.Artificial intelligence,education,and entrepreneurship[J].The Journal of Finance,2024,79(1):631-667.
• [7] MIAO F,CUKUROVA M.AI competency framework for teachers[EB/OL].https://unesdoc.unesco.org/ark:/48223/pf0000391104,html,2024.09.
• [8] MIAO F,SHIOHIRA K,LAO N,et al.AI competency framework for students[EB/OL].https://unesdoc.unesco.org/ark:/48223/pf0000391105,html,2024.09.
• [9] 吴岩.新工科：高等工程教育的未来，高等工程教育研究，2018,6:1-3.WU Y.New engineering:the future of higher engineering education[J].Research in Higher Education of Engineering,2018,6:1-3.(in Chinese)
• [10] 郑庆华.人工智能赋能STEM教育创新发展：认识与实践[J].中国高教研究，2025,(1):1-7.ZHENG Q H.Artificial intelligence empowering the innovative development of STEM education:insights and practices[J].China Higher Education Research,2025,(1):1-7.(in Chinese)
• [11] 章丽辉，钟圣怡，赵加强，等.新工科视域下数理基础课程的跨学科教学[J].高等工程教育研究，2019,(6):44-49.ZHANG L H,ZHONG S Y,ZHAO J Q,et al.Interdisciplinary teaching of foundation courses from the perspective of emerging engineering education[J].Research in Higher Education of Engineering,2019,(6):44-49.(in Chinese)
• [12] 王小力.大学物理课程思政研究与实践[J].中国大学教学，2020,10:54-57.WANG X L.Research and practice on ideological and political education in university physics courses[J].China University Teaching,2020,10:54-57.(in Chinese)
• [13] 何钰，孙燕云，谢东，等.基于知识图谱的大学物理课程建设与实践[J].物理与工程，2024,34(6):149-157.HE Y,SUN Y Y,XIE D,et al.Construction and practice of college physiscs curriculum based on knowledge graph[J].Physics and Engineering,2024,34(6):149-157.(in Chinese)
• [14] 张红光，李永涛，杨志红，等.基于知识图谱的“大学物理”AI课程建设与实践[J].大学物理，2025,44(5):41-47,75.ZHANG H G,LI Y T,YANG Z H,et al.,Construction andpractice of artificial intelligence course of university physics based on knowledge map[J].College physics,2025,44(5):41-47,75.(in Chinese)
• [15] 周可雅，孟庆鑫，曹永印，等.问题牵引式大学物理课程知识图谱探索与实践[J].大学物理，2025,44(1):66-69,75.ZHOU K Y,MENG Q X,CAO Y Y,et al.Exploration and practice based on a problem driven knowledge graph of college physics[J].College physics,2025,44(1):66-69,75.(in Chinese)
• [16] 唐泽宸，段文晖，徐勇.物理学与深度学习：2024年诺贝尔物理学奖介绍[J].物理，2025,54(1):1-9.TANG Z C,DUAN W H,XU Y.Physics and deep learning:an introduction to the 2024 Nobel Prize in Physics[J].Physics,2025,54(1):1-9.(in Chinese)
• [17] MILLER J L.Nobel Prize highlights neural networks' physics roots[J].Physics Today,2024,77(12):12-16.
• [18] 唐乾元.物理学与人工智能的连接：2024年诺贝尔物理学奖解析[J].科学通报，2025,70:1413-1420.TANG Q Y.The connections between physics and AI:A review of the 2024 Nobel Prize in physics[J].Chinese Science Bulletin,2025,70:1413-1420.(in Chinese)
• [19] KARNIADAKIS G E,KEVREKIDIS I G,LU L,et al.Physics-informed machine learning[J].Nature Reviews Physics,2021,3(6):422-440.
• [20] CHEN Z,YANG L,SUN H.Physics-informed learning of governing equations from scarce data[J].Nature Communications,2021,12.1:6136.
• [21] JIAO L,SONG X,YOU C,et al.AI meets physics:a comprehensive survey[J].Artificial Intelligence Review,2024,57:256.
• [22] 李艳，孙凌云，江全元，等.高校教师人工智能素养及提升策略[J].开放教育研究，2025,31(1):23-33.LI Y,SUN L Y,JIANG Q Y,et al.Artificial intelligence literacy of university educators and improvement strategics[J].Open Education Research,2025,31(1):23-33.(in Chinese)