Author(s)

Binbin Qiu ¹, Xin Zhang ¹, Yu Zhu ¹, Yinjun Zhao ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Binbin Qiu

ABSTRACT

With the rapid development of generative AI, intelligent systems—represented by large language models—are profoundly influencing the pedagogical models and learning methods of higher education. As a critical foundational course for engineering majors, Electrotechnics and Electronics is characterized by abstract theories, extensive mathematical derivations, and strong practical requirements. Traditional teaching models have increasingly revealed shortcomings in stimulating student interest and meeting personalized learning needs. Generative AI demonstrates significant advantages in areas such as intelligent tutoring, automated content generation, experimental and simulation assistance, and learning assessment, providing new opportunities for curriculum reform. However, its application in teaching also faces a series of challenges, including academic integrity, increased student dependency, the shifting role of educators, and the reconstruction of evaluation systems. This paper systematically analyzes the application scenarios of generative AI in the teaching of Electrotechnics and Electronics, explores the pedagogical transformations and potential risks it brings, and proposes corresponding strategies to provide a reference for the reform of related engineering courses.

KEYWORDS

Generative Artificial Intelligence (AI), Electrotechnics and Electronics, Teaching Reform, Engineering Education, Intelligent Teaching

CITE THIS PAPER

Binbin Qiu, Xin Zhang, Yu Zhu, Yinjun Zhao. Transformations and Challenges Brought by Generative AI to the Teaching of "Electrotechnics and Electronics". Curriculum and Teaching Methodology (2026) Vol. 9: 37-45. DOI: http://dx.doi.org/10.23977/curtm.2026.090106.

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