Construction of Intelligent Listening and Speaking Model for College Students with AI Empowerment

Abstract

Against the backdrop of deep integration between educational digital transformation and artificial intelligence technologies, cultivating college students' English listening and speaking competencies has become a central focus in contemporary foreign language teaching reforms. Traditional English listening and speaking instruction consistently faces significant challenges, such as insufficient personalization, delayed feedback mechanisms, and monotonous learning scenarios. Consequently, these conventional methods fundamentally fail to meet the dynamic learning needs of digitally native college students who require highly interactive environments. To overcome these limitations, this study establishes an innovative, intelligent English listening and speaking model specifically designed for college students. The proposed framework is rigorously based on the output-driven hypothesis, comprehensible input theory, and an integrated teaching-learning-evaluation paradigm. By strategically leveraging advanced AI technologies—including intelligent speech recognition, large language models, and comprehensive learning analytics—the model adopts a systematic "smart diagnosis-scenario-based implementation-dynamic evaluation-iterative optimization" approach. Through meticulously explaining its theoretical foundations, core architectural design, practical implementation pathways, and robust safeguard mechanisms, this study demonstrates the model's efficacy. Furthermore, coupled with empirical validation through typical application cases, the findings indicate substantial improvements in student engagement and communicative proficiency. Ultimately, this research provides vital theoretical references and actionable practical pathways to effectively address persistent challenges in college English teaching, thereby significantly enhancing overall talent cultivation quality in higher education.