The deepening of international collaboration in the field of electrical engineering puts forward higher requirements for the professional English ability. Traditional teaching suffers from weak academic writing, ineffective intensive reading of literature, and lack of international communication. This study builds an AI-assisted closed-loop training model that includes searching, reading, writing and expression. Through the design of classroom instructional content and team-based practical tasks, deep integration between professional English proficiency and engineering practice is achieved. This model significantly enhances students' technical documentation productivity and academic discourse competence, thereby providing a replicable, data-informed pedagogical pathway for the internationalization of engineering education.

Zhuoxin Lu, Weiqing Sun, Dong Han. AI-Driven Professional English for Electrical Engineering Teaching Design: Closed-Loop Training from Professional Literature Reading to International Academic Communication. Frontiers in Educational Research (2025), Vol. 8, Issue 9: 61-66. https://doi.org/10.25236/FER.2025.080911.

[1] Shirui Pan, Linhao Luo, Yufei Wang, et al. Unifying large language models and knowledge graphs: A roadmap[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7): 3580-3599.

[2] Jiali Lin and Jian Li. Personalized Learning in College English Education: Leveraging Large Language Models for Adaptive Instruction[C], 2025 2nd International Conference on Informatics Education and Computer Technology Applications (IECA), Kuala Lumpur, Malaysia, 2025: 119-125.

[3] Laleh Khojasteh, Reza Kafipour, Farhad Pakdel, et al. Empowering medical students with AI writing co-pilots: design and validation of AI self-assessment toolkit[J]. BMC Med Educ, 2025, 25: 159.