Integrating AI into the Pedagogy of Electronic Circuits Laboratory Course

Artificial intelligence technology, as a highly efficient auxiliary tool, is driving innovation in higher education teaching models. This paper focuses on the deep integration and application of AI technology in the experimental course of electronic circuit design. Addressing core issues such as the highly theoretical nature of the course, the abstract and difficult-to-understand components, and the insufficient development of students’ practical abilities, this study constructs and implements a new AI-integrated teaching model for the experimental course. By leveraging AI-assisted component recognition, knowledge graph construction, and learning analytics, this model comprehensively restructures the entire teaching process, including pre-class preparation, in-class instruction, experiment design, and teaching reflection. Empirical evidence shows that the introduction of AI tools significantly reduces the learning difficulty for students, provides a personalized and exploratory learning experience, enhances their understanding of circuit principles, and strengthens their capabilities in circuit design and fault diagnosis. This approach offers a new pathway and paradigm for the reform of basic electronic circuits laboratory courses.