Construction and Practice of an AI-Based Personalized Training Mode for College English Listening and Speaking

Abstract

The rapid development of artificial intelligence technology has opened new avenues for the reform of college English listening and speaking teaching. In response to the shortcomings of traditional teaching models, such as severe homogeneity and insufficient attention to individual student differences, this paper constructs an AI-based framework for personalized English listening and speaking training. Relying on data-driven diagnostic analysis of learning situations, dynamic adaptation of learning paths, and the integration of multimodal resources, the framework builds a virtual simulation environment, integrating speech recognition and semantic analysis technologies to achieve high-frequency immersive language practice. Case studies indicate that AI technology significantly enhances students’ oral fluency and listening comprehension, and stimulates learning initiative through an immediate feedback mechanism. However, current technology still faces challenges such as limited accuracy in speech recognition and insufficient algorithmic scenario adaptability. This paper proposes optimization strategies based on practical cases, providing theoretical reference for the digital transformation of education.