大学物理 ›› 2025, Vol. 44 ›› Issue (5): 41-.doi: 10.16854/j.cnki.1000-0712.240396

• 教学改革 • 上一篇    下一篇

基于知识图谱的“大学物理”AI课程建设与实践

张红光,李永涛,杨志红,陈伟,毕岚,董慧媛,王允辉,单俊豪   

  1. 1.南京邮电大学 理学院,江苏 南京210023;2. 南京邮电大学 教育科学与技术学院,江苏 南京210023
  • 收稿日期:2024-08-31 修回日期:2024-10-24 出版日期:2025-07-01 发布日期:2025-07-28
  • 作者简介:张红光(1985—),男,山东章丘人,南京邮电大学理学院副教授,博士,主要从事大学物理实验教学和多功能磁性材料物性及机理研究工作.
  • 基金资助:
    江苏本科高校“理工类公共基础课程教学改革研究”专项课题(2024LGJK041);南京邮电大学教学改革项目(JG00723JX58);江苏省教改研究课题一般项目(2023JSJG689)

Construction andpractice of artificial intelligence course of  university physics based on knowledge map

ZHANG Hongguang1, LI Yongtao1, YANG ZhiHong1, CHEN Wei1, BI Lan1, DONG Huiyuan1, WANG Yunhui1, SHAN Junhao2   

  1. 1. College of Physics, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China; 2.College of 

    Educational Sciences and Techonolgy, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China

  • Received:2024-08-31 Revised:2024-10-24 Online:2025-07-01 Published:2025-07-28

摘要: 当前,人工智能(AI)正深刻变革教育.我校“大学物理”团队在智慧树平台创新推出知识图谱驱动的AI课程,构建“师/生/机”互动新教学模式.本文概述师-机-生协同教学体系的设计理念、AI课程的建设、实施、成效及反思情况.从实践效果看,基于知识图谱的学习者画像及自适应学习为学生提供个性化的学习能够有效提高自主学习性和学习成绩,为理工科基础课AI课程建设提供了经验参考.

关键词: 人工智能, 大学物理, 知识图谱

Abstract: Currently, artificial intelligence (AI) is profoundly changing education. The University Physics team of our university has innovatively launched a knowledge mapdriven AI course on the Wisdom Tree platform, and constructed a new teaching mode of ‘teacher/student/machine’ interaction. This paper outlines the design concept of the teachermachinestudent collaborative teaching system, the construction, implementation, effectiveness and reflection of the AI course. From the practical effect, the learner image based on knowledge graph and adaptive learning provide students with personalized learning can effectively improve independent learning and learning performance, which provides an experience reference for the construction of AI courses in basic science and engineering courses.

Key words: artificial intelligence, university physics, knowledge mapping