Knowledge graph promotes the deep integration of artificial intelligence technology and education, providing a technical route and strategy for the digital construction and practice of courses. This paper demonstrates the ideas and processes of constructing a knowledge graph for university physics courses, and analyzes the impact and role of teaching goal orientation, cross course correlation, and ideological and political knowledge point correlation when extracting knowledge points and related knowledge points. By combining examples of university physics course knowledge graphs, the paper shows the generation methods of knowledge graphs, the forms of graph representation, and the settings of knowledge point associations. Based on teaching practice, it was demonstrated that students use knowledge graphs for personalized learning and complete self-evaluation. Teachers use knowledge graphs to implement a “blended online and offline” teaching approach in large-scale classes, providing real-time supervision, guidance, and feedback to students, achieving the empowerment of artificial intelligence technology to build first-class courses.
He et al. (Sun,) studied this question.