Exploration of AI-Enhanced Teaching Models in Microcontroller Courses

Dan Wang; Cong Yuan; Rui Guo

doi:10.54097/336me943

Authors

Dan Wang
Cong Yuan
Rui Guo

DOI:

https://doi.org/10.54097/336me943

Keywords:

Microcontroller, AI Integration, Personalized Learning Paths, Industry Alignment

Abstract

This paper discusses the integration of artificial intelligence (AI) into microcontroller principles and applications courses to enhance students' comprehensive abilities in response to industry needs. The "AI+" approach is introduced to redesign course content, focusing on application-based curricula and implementation pathways. Course content is structured around automotive electronics supply chains, integrating multiple disciplines including automotive electronics, embedded systems, and artificial intelligence. It is divided into three layers: foundational, application, and expansion, aiming to comprehensively develop students' basic knowledge and advanced skills. In teaching practices, AI-enabled collaborative development workflows are employed, emphasizing "need release - AI toolchain application - team collaboration" processes, forming a tripartite cooperation model: teacher guidance, student practice, and AI empowerment. To enhance resource integration and utilization, three tiers of teaching resources: course knowledge repositories, industry demand databases, and competition research libraries have been established. By incorporating AI into microcontroller courses, students are better prepared to meet both current and future engineering challenges, equipped with practical skills and encouraged to develop innovative capabilities.

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