THE BLENDED TEACHING REFORM OF “SIGNALS AND SYSTEMS” COURSE EMPOWERED BY ARTIFICIAL INTELLIGENCE

Aiming at the prominent pain points in the traditional teaching of Signals and Systems, such as compressed class hours, fragmented resources, weak practical links, and time and space constraints on teacher-student interaction, combined with the strategic requirements of the Ministry of Education for the development of “Artificial Intelligence+Higher Education”, this research carries out an exploration of AI-empowered blended teaching reform of the course. A “Four-in-One” blended curriculum system integrating classroom learning, AI teaching assistant accompanied learning, resource expansion and practical teaching is constructed. By deeply integrating generative AI large models, the three maps of knowledge, ability and quality with the BOPPPS teaching model, a smart teaching community with the ternary interaction of “Teachers-Students-Machines” is created. Taking the Sampling Theorem as an example, a 100-minute teaching implementation process is designed, with AI technology embedded in all links of teaching and learning from the pre-class BOP, in-class P to post-class PS. The research results show that this teaching reform model effectively solves the problems of traditional teaching, improves students’ engineering practical ability, autonomous learning ability and team cooperation ability, and at the same time enhances teachers’ teaching efficiency. The formed teaching plan has good popularizability and transplantability, providing a reference paradigm for the smart teaching reform of basic courses for electronic information majors.