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With the rapid development of artificial intelligence (AI) technology, the application of technologies represented by large language models and generative AI in the field of education has gradually become a research hot spot. In electrical engineering education, the complexity of course content and the limited availability of experimental resources pose challenges to teaching. This study designed and implemented an AI teaching assistant architecture tailored for electrical engineering courses, proposing a dual-sided framework aimed at supporting the improvement of student learning efficiency and the optimization of teaching management. Using electrical courses as a practical case study, the study explored the application value and implementation pathways of AI teaching assistants in electrical engineering education. Experimental results indicate that it has certain effects in grading accuracy, personalized learning, and teaching management. This study provides theoretical support and practical references for the digital-intelligent transformation of electrical engineering education, and the proposed architecture offers a new perspective for research on AI-driven educational innovation.

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Research on the Impact of AI Teaching Assistants on Electrical Engineering Courses—A Case Study of the Course “Exploring the World of Electricity”

  • 会议论文

  • 发表日期：

    2026年

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  • Kewei Shi,
  • Peidong Xu,
  • Jun Zhang,
  • Fei Tang,
  • Xiangtao Zhuan,
  • Xuzhu Dong,
  • Yuanfeng Chen

摘要

With the rapid development of artificial intelligence (AI) technology, the application of technologies represented by large language models and generative AI in the field of education has gradually become a research hot spot. In electrical engineering education, the complexity of course content and the limited availability of experimental resources pose challenges to teaching. This study designed and implemented an AI teaching assistant architecture tailored for electrical engineering courses, proposing a dual-sided framework aimed at supporting the improvement of student learning efficiency and the optimization of teaching management. Using electrical courses as a practical case study, the study explored the application value and implementation pathways of AI teaching assistants in electrical engineering education. Experimental results indicate that it has certain effects in grading accuracy, personalized learning, and teaching management. This study provides theoretical support and practical references for the digital-intelligent transformation of electrical engineering education, and the proposed architecture offers a new perspective for research on AI-driven educational innovation.