ARTIFICIAL INTELLIGENCE IN TFL: PRONUNCIATION AND ACCENT
Abstract
This research explores how AI-based pronunciation tools used in Teaching Foreign Languages (TFL) deal with different learner accents and speech clarity in Uzbekistan. Using ideas from Dugošija (2024) and Crompton et al. (2024), it reviews three popular TFL applications—ELSA Speak, Duolingo, and Speechling—and identifies issues related to calibration and inclusivity that often create difficulties for speakers with non-native accents. The study also introduces a new local model, the Inclusive Calibration Framework for Uzbek TFL Pronunciation (ICF-U), designed to make AI use more ethical, culturally sensitive, and technically accurate.
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References
- Crompton, H., et al. (2024). AI in Education: Calibration, Explainability, Inclusivity, and Bias (CEI&B).
- Dugošija, A. (2024). Ethical and Cultural Risks of AI in Teaching Foreign Languages.
- Kolegova, O., & Levina, E. (2024). Practical Benefits and Efficiency of AI in Teaching Foreign Languages.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt this work for any purpose, including commercially, provided you give appropriate credit to the original author(s) and source, provide a link to the license, and indicate if changes were made.
License: creativecommons.org
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