Research on Teaching Strategies for Electrical Automation Technology Vocational Education Based on Generative Artificial Intelligence
Year: 2026
Type: article
Abstract: Generative Artificial Intelligence (GAI), as a machine learning system capable of deeply mining data patterns and automatically generating multimodal content, has become a core disruptive technology in digital transformation, reshaping cognitive paradigms and practical pathways in the field of education. In vocational education for the Electrical Automation Technology major, how to effectively leverage GAI to optimize curriculum structures, enhance teaching quality, and strengthen practical skills remains a critical issue facing the discipline's development. From the perspective of the reconstruction of teaching elements driven by technological and environmental changes, this paper proposes teaching strategies for Electrical Automation Technology vocational education based on GAI. These strategies include constructing a core curriculum system closely aligned with the technical requirements of electrical automation positions, optimizing course design through cognitive augmentation technologies, utilizing digital humans for collaborative practical training, and creating a new teaching ecosystem based on multimodal intelligent interaction. Collectively, these strategies give rise to an adaptive learning framework characterized by "data-driven precision — algorithm-based intelligent adaptation — dynamic continuous evolution," promoting the transformation of professional teaching from traditional knowledge-transfer models to autonomous construction models of cross-domain knowledge networks. This provides theoretical support and practical guidance for improving teaching quality and efficiency, meeting market demands, and cultivating applied skilled talents with innovative spirit and practical capabilities.
DOI: 10.63313/ijsseh.9035
Authors: Jun Yu, Xiaopin Xu
Institution: Wenzhou Polytechnic
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7162342015