With the rapid development of artificial intelligence technology, AI translation software has become increasingly widespread in the field of education, particularly demonstrating significant potential in multilingual professional teaching at vocational colleges. However, existing AI translation tools lack adaptability and interactivity in educational scenarios, failing to meet the dual needs of teacher guidance and student-driven learning. This paper addresses the practical needs of vocational education by developing an AI translation software human-machine collaboration system that integrates teacher intervention mechanisms, learner perception models, and multimodal interactive interfaces. It also proposes an interaction model optimized for educational settings. The system integrates natural language processing, cognitive adaptation analysis, and visualization feedback mechanisms. Through teacher-led human-machine collaboration control, student-participatory learning paths, and interface optimization design, it effectively enhances translation accuracy and teaching interaction efficiency. Experimental results indicate that the system significantly improves students' language comprehension abilities and participation enthusiasm in vocational English teaching. The study provides theoretical support and practical pathways for the deep integration of AI translation technology in vocational education.
Li Qin (Sun,) studied this question.
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