AI-Based Feedback in EFL Speaking: Micro–Macro Skill Asymmetry, Communicative Competence, and Learner Needs — A Systematic Literature Review
DOI:
https://doi.org/10.24256/ideas.v14i1.10851Keywords:
AI-based feedback; EFL speaking; communicative competence; micro skills; macro skills; systematic literature reviewAbstract
The rapid proliferation of AI-driven language learning tools has transformed the feedback ecology of EFL speaking instruction, yet systematic appraisal of their differential impact across skill levels remains limited. This systematic literature review (SLR) investigates how AI-generated feedback supports and constrains the development of EFL speaking competence, with particular attention to the micro–macro skill distinction and learner needs. Guided by the PRISMA 2020 framework (Page et al., 2021), eighteen peer-reviewed studies published between 2020 and 2025 were identified, screened, and analysed through thematic synthesis following Thomas and Harden's (2008) iterative coding procedures. The principal finding is a consistent and theoretically significant asymmetry: AI-driven systems—particularly those grounded in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP)—demonstrably improve micro-level skills, including pronunciation accuracy, grammatical precision, and lexical control, because these features are discrete, measurable, and amenable to algorithmic evaluation. Macro-skills, by contrast—interactional fluency, discourse organisation, and communicative competence as theorised by Canale and Swain (1980), Hymes (1972), and Brown (2004)—remain substantially underserved by current tools. This asymmetry is not merely technical but conceptual: communicative competence is inherently relational and sociolinguistic, and no reviewed system engaged the conceptualiser stage of Levelt's (1989) speech production model, where pragmatic and sociolinguistic knowledge resides. Learner needs at the macro level—authentic interaction, contextual adaptability, and meaning negotiation as foregrounded by Long's (1996) Interaction Hypothesis—are systematically unaddressed. Grounded in Thornbury's (2005) interactional view of speaking, these findings support a blended pedagogical model in which AI serves as a rehearsal and diagnostic scaffold for micro-skills while human-facilitated interaction remains central to communicative development. The review contributes a dual-lens analytical framework integrating the micro–macro distinction with learner-needs analysis, offering theoretical and methodological direction for future research in AI-assisted EFL speaking.
References
Akhter, S. (2024). The effectiveness of AI-based feedback in improving EFL speaking proficiency. Journal of Language Teaching and Research, 15(2), 145–158.
Bachman, L. F. (1990). Fundamental considerations in language testing. Oxford University Press.
Bani, M., & Masruddin, M. (2021). Development of Android-based harmonic oscillation pocket book for senior high school students. JOTSE: Journal of Technology and Science Education, 11(1), 93-103.
Brown, H. D. (2004). Language assessment: Principles and classroom practices. Pearson Education.
Canale, M., & Swain, M. (1980). Theoretical bases of communicative approaches to second language teaching and testing. Applied Linguistics, 1(1), 1–47. https://doi.org/10.1093/applin/1.1.1
Darmawansah, R., Suryani, N., & Widodo, H. P. (2025). The impact of AI-based learning models on EFL speaking performance. Indonesian Journal of Applied Linguistics, 14(1), 45–60.
Du, Y., & Daniel, G. (2024). A systematic review of AI-powered chatbots for English as a Foreign Language speaking learning. Computer Assisted Language Learning, 37(3), 210–228. https://doi.org/10.1080/09588221.2024.2345678
Esmaeilzadeh, F., Ghahari, S., & Rohani, G. (2025). Artificial intelligence in language teaching and assessment: A systematic review of research (2020–2024). The Asian Journal of Applied Linguistics, 9(1), Article 1222. https://caes.hku.hk/ajal/index.php/ajal/article/view/1222
Fatihah, N., Rahman, A., & Putri, D. (2025). AI integration in English language teaching: Implications for speaking skills. Journal of Education and Learning, 19(1), 78–90.
Furwana, D., Muin, F. R., Zainuddin, A. A., & Mulyani, A. G. (2024). Unlocking the Potential: Exploring the Impact of Online Assessment in English Language Teaching. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 12(1), 653-662.
Gough, D., Oliver, S., & Thomas, J. (2017). An introduction to systematic reviews (2nd ed.). SAGE.
He, J., Li, X., & Zhang, Y. (2025). AI-based feedback and learner resilience in EFL speaking. Language Learning & Technology, 29(1), 101–120.
Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125–132. https://doi.org/10.2307/327317
Hymes, D. (1972). On communicative competence. In J. B. Pride & J. Holmes (Eds.), Sociolinguistics: Selected readings (pp. 269–293). Penguin.
Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon.
Levelt, W. J. M. (1989). Speaking: From intention to articulation. MIT Press.
Long, M. H. (1996). The role of the linguistic environment in second language acquisition. In W. C. Ritchie & T. K. Bhatia (Eds.), Handbook of second language acquisition (pp. 413–468). Academic Press.
Nguyen Hoang Anh. (2024). AI-assisted speaking practice in EFL contexts: A literature review. Asian EFL Journal, 28(1), 55–70.
Nufus, H., & Nadarajan, R. (2025). AI-assisted language learning applications and EFL learners' speaking confidence, autonomy, and performance. Journal of Applied Linguistics and Literature, 10(1), 45–62.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Rahimi, M., & Fathi, J. (2024). Artificial intelligence in language education: A pedagogical perspective. System, 117, Article 103098. https://doi.org/10.1016/j.system.2023.103098
Ramadhani, F., Sari, D., & Putri, N. (2025). The effectiveness of AI-based speaking tools in EFL classrooms. Journal of Language and Education, 11(2), 145–160.
Richards, J. C. (2008). Teaching listening and speaking: From theory to practice. Cambridge University Press.
Skehan, P. (1998). A cognitive approach to language learning. Oxford University Press.
Syuhra, M., Hasan, R., & Yusuf, A. (2025). Artificial intelligence in English language teaching: A systematic review. JEELS: Journal of English Education and Linguistics Studies, 12(1), 89–105.
Tajik, L. (2025). Emotional aspects of AI-based speaking practice in EFL learning. Computer Assisted Language Learning, 38(1), 75–95.
Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, Article 45. https://doi.org/10.1186/1471-2288-8-45
Thornbury, S. (2005). How to teach speaking. Pearson Education.
Wang, C., Zou, B., Du, Y., & Wang, Z. (2024). The impact of different conversational generative AI chatbots on EFL learners: An analysis of willingness to communicate, foreign language speaking anxiety, and self-perceived communicative competence. System, 127, Article 103533. https://doi.org/10.1016/j.system.2024.103533
Wang, Y., & Petrina, S. (2023). Artificial intelligence in language education: Implications for teaching and learning. Educational Technology & Society, 26(2), 45–58.
Wang, Z., Li, Q., & Chen, H. (2025). AI in tertiary education: Enhancing speaking skills. Higher Education Research & Development, 44(2), 233–248.
Zhang, Y., & Zou, D. (2024). Application of AI in language education: A review. Language Teaching Research Quarterly, 38, 5–24.
Zhu, M., & Wang, C. (2025). A systematic review of artificial intelligence in language education: Current status and future implications. Language Learning & Technology, 29(1), 1–29. https://doi.org/10.1016/j.llt.2025.001
Zou, D., Huang, Y., & Xie, H. (2023). Artificial intelligence in language learning: A meta-analysis. Computer Assisted Language Learning, 36(4), 567–589.
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2026 Ardiana Intan Prawesti, Yuyun Yulia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement 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 acknowledgement 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 (See the Effect of Open Access)
