The emergence of large language models (LLMs) is profoundly transforming several fields, including education. Currently, LLMs have been successfully utilized to assist in general education, liberal arts, and computer science teaching fields. However, the application of LLMs in power electronics experimental teaching still faces obstacles, challenged by issues such as inaccurate circuit topology recognition and omission of key circuit information, including circuit component connections. Therefore, this paper proposes an innovative artificial intelligence method for power electronics experimental teaching, leveraging the synergistic combination of intelligent plugins and LLMs to achieve accurate circuit topology recognition, information extraction, knowledge verification, and content generation. The primary goal is to overcome existing limitations and enhance the efficiency and quality of experimental teaching.

The proposed method first trains a deep learning model, namely YOLO, to accurately identify the components, nodes, and positions of the circuit topology. Then, it designs an algorithm to translate this information into circuit netlist text, which is recognizable by LLMs. This netlist, along with a series of carefully crafted prompts, is presented to the LLM to guide it in completing various tasks, including netlist verification, circuit analysis, and document generation. The proposed method encapsulates circuit topology recognition and netlist generation into “intelligent plugins,” compensating for the inherent shortcomings of the LLM. This paper also designs a series of prompts to guide the LLM in effectively performing its designated teaching functions, such as generating experimental instructions for teachers and generating preview Q&A sets for students. To ensure cooperation among the intelligent plugins, prompts, and LLM, the workflow of the proposed method is also presented. In addition, a software tool named “PE Exp. Agent,” which can be integrated with various LLMs through an API interface, vividly demonstrates the aforementioned design philosophy.

This software tool is applied to several stages of power electronics experimental teaching. The intelligent plugins accurately identify the circuit topology from the published paper and correctly generate the corresponding netlists. By applying different prompts, the LLM generates different content. In particular, different LLMs are called on for extensive testing, and the results showed that, although differences exist in the style and expression among different LLMs, they always remain consistent with the requirements set by the prompts. Thanks to the natural language understanding capabilities of LLMs, any dissatisfaction can be quickly addressed and corrected through interaction with the LLM. Therefore, the proposed method considerably reduces the workload of power electronics experimental teaching personnel and improves the timeliness and quality of experimental teaching.

The integration of intelligent plugins and LLMs provides a novel and effective method for the design of power electronics experimental teaching. It not only lightens the burden on teachers but also improves the overall quality of teaching. By developing software tools similar to “PE Exp. Agent,” the efficiency and effectiveness of experimental teaching can be effectively promoted. With modifications to the plugins and prompts, this method can also adapt to the needs of more teaching tasks. The method proposed in this paper shows potential as a new productivity tool for the design of power electronics experimental teaching.