Artificial Intelligence (AI) in Enhancing Spoken English Proficiency: A Systematic Literature Review
DOI:
https://doi.org/10.32585/ijelle.v6i2.5980Abstract
This systematic review explores the integration of Artificial Intelligence (AI) tools in teaching spoken English to EFL learners, with an emphasis on intelligent tutoring systems, speech recognition applications, and technologies providing adaptive feedback. A comprehensive search of academic databases, primarily Google Scholar, initially identified 17,200 articles on AI in English learning. By applying Boolean techniques, this number was narrowed down to 45 articles specifically addressing AI tools for spoken English learning in Indonesia. After applying inclusion and exclusion criteria, 14 articles published in national and international journals, were chosen for further review. A qualitative analysis of these studies revealed key themes such as the benefits of AI tools in enhancing students’ spoken English proficiency and challenges in implementation. The review highlights emerging trends, identifies research gaps, and offers recommendations for optimizing AI-driven approaches in EFL contexts. The findings emphasize AI's potential to enhance spoken English proficiency while also underscoring areas that require further attention to optimize their application. Effective strategies for integrating AI tools with instructional practices should be examined in future research to optimize learner engagement and enhance spoken English proficiency.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ainol Mardhiah, Diana Purwati, Lathifatuddini, Hayatul Muna, Helmiyadi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with the International Journal of English Linguistics, Literature, and Education (IJELLE) agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.