Exploration on Teaching Reform of AI-enabled Automatic Control Principle Course

Abstract

Aiming at the problems of abstract knowledge structure, lack of intuitive connection between chapters, disconnection between theoretical analysis and engineering practice, and lag of learning evaluation feedback in the course of automatic control principle, this paper explores the teaching reform path of artificial intelligence (AI) empowerment based on the concept of outcome-based education. The course knowledge graph is constructed with modeling, analysis, correction and verification as the main line, and the semantic association between knowledge nodes, prerequisite dependence, error-prone concepts and engineering cases is designed. Through the large language model to assist in answering questions, learning portraits and hierarchical task recommendation, the teaching closed-loop of pre-school diagnosis, pre-class guidance, in-class inquiry, after-class practice and feedback is constructed to improve teaching; based on typical engineering projects such as motor speed regulation, the control theory derivation, MATLAB/Simulink simulation and artificial intelligence assisted analysis are combined. A multi-evaluation mechanism covering knowledge understanding, independent reasoning, simulation design, engineering interpretation and academic norms is established. The specific implementation plan of knowledge map construction, classroom activity organization, teaching effect forensics and risk management is put forward in order to provide an operable reference for the digital intelligence reform of control basic courses.