Construction and Practical Exploration of a Generative AI-Driven "Human-Computer Collaboration" Teaching Model in Higher Education —— Taking the Course "Electrical and Electronic Technology and Applications" as an Example

Addressing the core challenges commonly faced by students majoring in Mechanical Design, Manufacturing, and Automation when learning the course "Electrical and Electronic Technology and Applications," such as "emphasizing machinery over electricity, having a weak foundation in high-power systems, and a disconnection between theory and practice," this paper employs Activity Theory as an analytical framework. It views the teaching activity as a dynamic system composed of "Subject (teachers and students) - Tool (AI) - Object (student competency) - Community - Rules - Division of Labor." Based on this framework, this study systematically constructs a generative AI-driven "Human-Computer Collaboration" teaching model (HCMAS). This model redefines the roles and division of labor for teachers, students, and AI across three stages: "pre-class exploration, in-class internalization, and post-class transfer." In the teaching practice of the "Electrical and Electronic Technology and Applications" course, using typical mechanical system control problems (such as motor control and PLC applications) as carriers, the model guides students to transform abstract theoretical knowledge of electrical and electronic technology into the ability to solve mechanical engineering problems through collaboration with and critical evaluation of AI. Practice has shown that this model effectively enhances students' learning engagement, engineering thinking skills, and human-computer collaboration literacy. This research provides a valuable paradigm reference for the intelligent transformation and teaching innovation practices of other fundamental engineering courses within the context of New Engineering Education construction.