AI-BASED SOLUTIONS TO EMERGING ISSUES IN ESL PRONUNCIATION TRAINING

Abstract

Recent advances in Artificial Intelligence have challenged long-established assumptions about how pronunciation should be taught and learned in ESL classrooms. Rather than replacing traditional instruction, AI complements it by offering adaptive and data-informed feedback that addresses the limitations of conventional teaching methods. This paper explores the theoretical foundations and pedagogical implications of implementing AI-driven tools such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) for enhancing phonetic competence. Emphasis is placed on how these technologies support learner autonomy, provide real-time corrective feedback, and facilitate individualized pronunciation development. The discussion also considers socio-constructivist perspectives, underscoring the importance of interaction, scaffolding, and human–AI collaboration in effective pronunciation pedagogy. While AI applications demonstrate substantial potential for improving accuracy and motivation, challenges related to algorithmic fairness, privacy, and teacher mediation remain central to their responsible integration.