Design and Pilot Study on the Application of AI-Powered Adaptive Dialogue Systems for Personalized Vocational English in Technical Higher Vocational Colleges

In this study, we aim to solve individual differences and the lack of contextualized practice in vocational English learning in higher technical vocational colleges. We design and build a pilot AI-based adaptive dialogue system (AI-ADS). It uses natural language processing (NLP), speech recognition, and deep learning technologies. The system builds a scenario-based corpus that covers core majors such as equipment manufacturing, electronic information, and architectural engineering. It realizes dynamic difficulty adjustment, immediate feedback, error correction, and vocational context simulation. We carried out pilot studies on science and engineering major students in a higher vocational college. The results show that the system effectively improves learners’ oral fluency (+24.3%), professional vocabulary mastery (+31.7%), and learning motivation (satisfaction rate reaching 86.5%). This provides an effective technical solution for individualized deficiencies in higher vocational English teaching.