Research on Innovation of AI-Assisted Classroom Teaching Mode in Communication Electronic Circuits

Abstract

The communication electronic circuits feature includes high-frequency signal processing and multi-module coupling, which has non-linear cognitive load distribution in classroom teaching with inherent shortcomings when using traditional lectures to solve break points in deductive logic or no immediate feedback. Taking an artificial intelligence assistance perspective according to analyzing the mapping relationship between the course's knowledge structure and students' cognitive load, we defined the functional position and technical boundary of artificial intelligence, built a behavioral sequence based cognitive diagnosis model, a parameterized deduction intervention strategy, and a multimodal learning-state fusion mechanism, providing optimization path including completion of logical chain breaks, generative topological mutation prompts, and personalization content reorganization. Our research tries to provide a reference analytical framework and implementation ideas on human-machine collaboration teaching for high-frequency electronic circuit courses.