Exploration of Teaching and Practical Reform Paths for Power Electronics Technology Courses Empowered by Artificial Intelligence Technology

Jiazhi Lei, Jin Ma and Yue Wu

Education Insights, 2025, vol. 2, issue 11, 349-355

Abstract: In response to the evolving demands for power engineering professionals under the dual pressures of global energy transition and widespread digitalization, traditional power electronics technology courses face an urgent need for comprehensive teaching reform. This paper systematically investigates the reform pathways for these courses, emphasizing the integration of artificial intelligence (AI) technologies to enhance both instructional effectiveness and student competence. A three-dimensional reform framework is proposed, which centers on knowledge graphs to structure and visualize complex technical knowledge, is supported by AI-powered teaching assistants to provide adaptive guidance and personalized learning experiences, and is enriched by virtual simulation environments to enable hands-on practice and experimentation beyond conventional laboratory limitations. Through in-depth analysis of practical implementation cases across multiple universities, the study demonstrates that this AI-empowered teaching model significantly improves students' understanding of theoretical concepts, strengthens their engineering problem-solving abilities, and fosters practical skills essential for contemporary power system applications. Moreover, this approach facilitates more interactive and engaging learning, promotes collaborative knowledge construction, and offers educators flexible tools for monitoring and optimizing learning outcomes. The findings highlight the replicability and scalability of this model, suggesting its potential as a paradigm for advancing engineering education reform in the era of intelligent technology and digital transformation, ultimately contributing to the cultivation of highly competent, innovation-oriented power engineering talents.

Keywords: power electronics; artificial intelligence; educational reform; knowledge graph; virtual simulation (search for similar items in EconPapers)
Date: 2025
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