The practical teaching of electrical and electronic engineering (EEE) is essential for cultivating students’ engineering competence. However, it is persistently constrained by large class sizes, limited laboratory resources, stringent safety requirements, and the difficulty of providing timely, individualized guidance. Recent advances in large language models (LLMs), particularly open-source reasoning models such as DeepSeek-R1, coupled with the emergence of tool-using autonomous agents, offer a promising avenue for addressing these challenges. This paper proposes a practical teaching mode for EEE based on DeepSeek agents. This study designs a multi-agent architecture comprising a concept-explanation agent, an experiment-planning agent, a simulation and tool-use agent, a Socratic tutoring agent, and an assessment agent. These agents are orchestrated around concrete electrical and electronic tasks, such as circuit analysis, analog amplification, and digital logic design. A case study implemented in two undergraduate courses demonstrates the workflow. A mixed-methods evaluation indicates that the proposed mode improves learning engagement, shortens feedback latency, and enhances error-diagnosis ability. It also reveals risks, such as model hallucination and student over-reliance, which must be managed through human-in-the-loop safeguards. The findings provide a transferable blueprint for integrating open-source reasoning agents into hands-on engineering education.
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Share and Cite
Hou, Q. (2026) Exploring a Practice Teaching Mode for Electrical and Electronic Engineering Based on DeepSeek Agents. Global Education Bulletin, 3(2), 131-137.