AI-Enhanced Reform of English Listening and Speaking Pedagogy in Vocational Education: Theoretical Construction and Empirical Validation

This quasi-experimental study investigated the effectiveness of an AI-enhanced pedagogical model for vocational English listening and speaking instruction. Grounded in constructivist learning theory and precision teaching principles, the model addresses persistent challenges of assessment difficulties, limited interaction, and lack of individualization in traditional vocational English education. A sample of 472 vocational college students participated in a semester-long intervention employing mixed-methods analysis. Results demonstrated significant improvements in listening and speaking competencies with effect sizes ranging from 0.68 to 0.98 (p<0.001). The experimental group achieved a 10% mean score increase, with the excellent performance rate rising from 11.4% to 31.6%. AI-driven platforms significantly enhanced cognitive engagement (d=0.344) and behavioral engagement (d=0.653), though emotional engagement showed limited improvement. The multimodal speech recognition system achieved 97.5% accuracy, significantly outperforming traditional assessment methods (p<0.05). Findings reveal that AI technologies effectively overcome spatial, temporal, and evaluative constraints of conventional listening-speaking instruction through intelligent assessment, immediate feedback, and personalized content delivery. Integrating international research trends with Chinese vocational contexts, this study proposes a localized pedagogical pathway combining Production-Oriented Approach (POA) with ideological-political education, providing systematic theoretical frameworks and empirical evidence for digital transformation in vocational education.