Modern Social Science Research

This study restructures the English testing model for secondary vocational schools in Guangxi, combining Communicative Language Testing (CLT) theory with cutting - edge AI tech. It uses a mixed - methods approach, including quantitative questionnaires, qualitative interviews, and experimental data analysis, to compare the CLT - based AI - powered model with the traditional one. The focus is on student performance, teaching feedback, and grasp of specialized vocabulary. The study shows a significant positive link between the new model and students all - round language skills in listening, speaking, reading, and writing. The AI scoring system makes assessment more objective and efficient. The personalized feedback system helps students better learn professional vocabulary and promotes their motivation and self - reflection. Through scenario - based tasks and multimodal data analysis, the model effectively bridges the gap between learning and practical use, offering useful references for vocational English assessment reform.

Communicative language testing; Artificial intelligence; Validation of validity; Washback effect; English education in secondary vocational schools