A CONCEPTUAL REVIEW OF AI-DRIVEN IPA INSTRUCTION FOR UZBEK EFL LEARNERS

Abstract

In the contemporary landscape of English as a Foreign Language (EFL) instruction in Uzbekistan, pronunciation remains a significant barrier to communicative competence.Uzbek learners face a profound "phonological gap" due to structural disparities between the Turkic morphology of Uzbek and the Germanic roots of English, resulting in persistentinterference errors. This conceptual review explo res the integration of Artificial Intelligence (AI) and Computer-Assisted Pronunciation Training (CAPT) to bridge this gap.By leveraging Automatic Speech Recognition (ASR) and visual feedback mechanisms, AI tools offer the individualized drilling necessary to master International Phonetic Alphabet (IPA) features. Furthermore, this paper proposes a pedagogical shift toward a "Flipped Classroom" model, integrating Mamatova’s (2023) Project-Based Learning (PBL) framework. This hybrid approach utilizes AI for home-based technical accuracy and reserves classroom time for meaningful, communicative interaction, ultimately reducing the affective filter and fostering learner autonomy