,
Senior Lecturer, Department of English Language Teaching Methodology, Termez State University , Termez , Uzbekistan
,
Fergana Medical Institute of Public Health, Department of Uzbek and Foreign Languages , Fergana , Uzbekistan
,
Faculty of Foreign Languages, Karshi State University , Karshi , Uzbekistan
,
Gulistan State Pedagogical Institute, Gulistan State University, Syrdarya Region , Gulistan , Uzbekistan
,
Associate Professor, Tashkent Kimyo International University , Tashkent , Uzbekistan
,
Department of Foreign Language and Literature, Termez University of Economics and Service , Termez , Uzbekistan
Associate Professor, Department of Technology and Geography, Termez State Pedagogical Institute , Termez , Uzbekistan
The ongoing challenge of learning non-native phonology in the synchronous digital learning setting is a primary obstacle to intelligible pronunciation and communicative competence. Conventional online teaching is rarely accompanied by real-time, personalized corrective feedback, leading to the persistence of fossilized pronunciation errors and poor phonological accuracy. The proposed study is an Artificial Intelligence (AI)-augmented neural feedback model that will be used to improve the accuracy of the acquisition of non-native phonological features by leveraging adaptive acoustic modelling and real-time articulatory feedback. The system combines deep neural networks trained on 12,500 annotated speech samples across 8 phonological categories and allows detection of segmental and suprasegmental deviations, achieving a mean phoneme recognition accuracy of 94.3%. In a synchronous virtual classroom environment, 120 learners were split into 60 control and 60 experimental groups during an 8-week intervention period, which was used to conduct a quasi-experimental evaluation. Students who received AI-enhanced neural feedback showed 31.8% higher phoneme-level accuracy, 24.6% lower prosodic deviation scores, and a statistically significant increase in intelligibility ratings (p < 0.01) compared with students receiving traditional instructor-based feedback. Sustained, real-time interaction was achieved with a latency of under 180 ms. Additionally, scores for learner engagement improved by 22.4%, demonstrating greater motivation and ongoing participation. Data-driven neural feedback systems coupled with AI and synchronous frameworks greatly improve learning accuracy for learners' phonology and put a promising approach to non-native pronunciation systems within the context of intelligent language learning.
This is an open access article distributed under the Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
0
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.