Personalized Oral English Learning Based on Generative Artificial Intelligence

Abstract

In the context of the rapid advancement of Generative Artificial Intelligence (GAI) technology and the intelligent transformation of education, traditional oral English learning is plagued by such enduring dilemmas as a one-size-fits-all approach, insufficient practice opportunities, delayed feedback, and anxiety about speaking up, making it difficult to meet learners’ personalized needs and the requirements for cultivating international competence. Generative Artificial Intelligence, however, offers a brand-new solution to these challenges with its robust natural language processing, multimodal interaction, and adaptive learning capabilities. Grounded in constructivist learning theory, second language acquisition theory, and metacognitive theory, this paper reviews the current research on the integration of Generative Artificial Intelligence into oral foreign language pedagogy. Focusing on six core dimensions—personalized learning demand analysis, customized learning corpus generation, interactive and authentic learning experience, intensive language application practice, intelligent assessment with real-time feedback, and dynamic adjustment of learning plans, this study systematically examines the application pathways and implementation strategies of Generative Artificial Intelligence in personalized oral English learning by combining specific cases of application tools. As an auxiliary tool, Generative Artificial Intelligence drives the transformation of education and the innovation of teaching models, providing an efficient solution for personalized oral English teaching. This research enriches the theoretical framework of GAI-empowered language education, and offers practical references for promoting the personalized and intelligent reform of oral English teaching.