Fostering AI Tools in Oral Fluency Developing among Khmer(1) EFL University Students: A Focus on English for Academic Purposes (EAP)
DOI:
https://doi.org/10.26877/eternal.v17i2.4047Keywords:
AI Tools, Oral Fluency Developing, Khmer EFL University Students, English for Academic Purposes (EAP)Abstract
Artificial intelligence (AI)-assisted speaking tools have increasingly been integrated into English for Academic Purposes (EAP) instruction to support oral communication. However, limited research has examined how AI-mediated speaking practice influences oral fluency while simultaneously addressing speaking anxiety and learner autonomy among Khmer EFL university students. This study investigates the effects of AI-mediated speaking practice on the oral fluency, speaking anxiety, and learner autonomy of ten Khmer EFL university students. Using a qualitative case study with embedded quantitative fluency analysis, participants completed nine AI-assisted speaking sessions over a three-week intervention using the EAP Talk platform. Oral fluency was assessed through Praat acoustic analysis focusing on speaking ratio, repair phenomena, and pause placement, while reflective questionnaires and semi-structured interviews explored learners' perceptions of anxiety and autonomy. The findings indicate that participants demonstrated longer speaking duration and fewer repair phenomena following the intervention, while pause placement shifted toward more meaningful syntactic boundaries. Questionnaire and interview data also revealed reduced speaking anxiety and improved learner autonomy during AI-mediated practice. These findings suggest that AI-assisted speaking practice can provide supportive opportunities for developing oral fluency while fostering greater confidence and self-directed learning in EAP contexts. Nevertheless, the findings should be interpreted cautiously because of the small sample size and the absence of a comparison group. Future studies should employ larger samples, longer intervention periods, and experimental designs to provide stronger evidence regarding the effectiveness of AI-assisted speaking practice.References
Banister, C. (2020). Exploring peer feedback processes and peer feedback meta-dialogues with learners of academic and business English. Language Teaching Research, 27(3), 746-764. https://doi.org/10.1177/1362168820952222.
Barkhuizen, G., Benson, P., & CHIK, A. (2014). Narrative inquiry in language teaching and learning research (Second language acquisition research series). Routledge.
Bashori, M., van Hout, R., Strik, H., & Cucchiarini, C. (2022). Web-based language learning and speaking anxiety. Computer Assisted Language Learning, 35(5–6), 1058–1089. https://doi.org/10.1080/09588221.2020.1770293.
Boersma, P., & Weenink, D. (2023). Praat: Doing phonetics by computer [Computer software]. University of Amsterdam. https://www.fon.hum.uva.nl/praat/.
Chafe, W. (1987). Cognitive constraints on information flow. In R. Tomlin (Ed.), Coherence and grounding in discourse (pp. 21-51). John Benjamins.
Chafe, W. (1994). Discourse, consciousness and time. University of Chicago Press.
Chafe, W. (2015). Constraining and guiding the flow of discourse. In D. Tannen, H. Hamilton, & D. Schiffrin (Eds.), The handbook of discourse analysis (Vol. 2; pp. 391-405). Wiley-Blackwell.
Chen, H. H. J., Yang, C. T. Y., & Lai, K. K. W. (2023). Investigating college EFL learners’ perceptions toward the use of Google Assistant for foreign language learning. Interactive Learning Environments, 31(3), 1335-1350. https://doi.org/10.1080/10494820.2020.1833043
Creswell, J. W. (2013). Qualitative inquiry & research design: Choosing among five approaches (3rd ed.). SAGE Publications.
DeKeyser, R. M. (1997). Beyond explicit rule learning. Automatizing second language morphosyntax. Studies in Second Language Acquisition, 19(2), 195–221. https://doi.org/10.1017/S0272263197002040.
DeKeyser, R. (2020). Skill acquisition theory. In B. VanPatten, G. D. Keating, & S. Wulff (Eds.), Theories in second language acquisition (pp. 83-104). Routledge.
Duff, P. (2018). Case study research in applied linguistics. Routledge.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Jiang, M. Y. C., Jong, M. S. Y., Lau, W. W. F., Chai, C. S., & Wu, N. (2023). Exploring the effects of automatic speech recognition technology on oral accuracy and fluency in a flipped classroom. Journal of Computer Assisted Learning, 39(1), 125-140. https://doi.org/10.1111/jcal.12732
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science (American Association for the Advancement of Science), 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415.
Kakitani, J., & Kormos, J. (2024). The effects of distributed practice on second language fluency development. Studies in Second Language Acquisition, 46(3), 770-794. https://doi.org/10.1017/S0272263124000251
Liu, J., Liu, X., & Yang, C. (2022). A study of college students’ perceptions of utilizing automatic speech recognition technology to assist English oral proficiency. Frontiers in Psychology, 13, 1-9. https://doi.org/10.3389/fpsyg.2022.1049139
Liu, X. J., Wang, J., & Zou, B. (2025). Evaluating an AI speaking assessment tool: Score accuracy, perceived validity, and oral peer feedback as feedback enhancement. Journal of English for Academic Purposes, 75, 1-14. https://doi.org/10.1016/j.jeap.2025.101505.
Liu, Y., binti Ab Rahman, F., & binti Mohamad Zain, F. (2025). A systematic literature review of research on automatic speech recognition in EFL pronunciation. Cogent Education, 12(1), 1-15. https://doi.org/10.1080/2331186X.2025.2466288
Meidyana, E. D., Salsabila, F. A., & Harris, S. (2025). AI for all voices: A scoping review on inclusive pronunciation and speaking AI tools. English Language and Literature International Conference (ELLiC) Proceedings, 8, 428-441.
Nassaji, H. (2016). Anniversary article interactional feedback in second language teaching and learning: A synthesis and analysis of current research. Language Teaching Research, 20(4), 535-562. https://doi.org/10.1177/1362168816644940.
Ochoa, X., & Zhao, H. (2024). OpenOPAF: An open-source multimodal system for automated feedback for oral presentations. Journal of Learning Analytics, 11(3), 224-248. European Second Language Association, 7(1), 60–74. Psycholinguistics, 39(3), 593-617. https://doi.org/10.18608/jla.2024.8411
Rood, M. M. S. J., & de Jong, N. H. (2023). A mixed-methods study on the effects of manipulated speaking anxiety on L2 utterance and L2 cognitive fluency. Journal of the European Second Language Association, 7(1), 60–74. https://doi.org/10.22599/jesla.96
Saito, K., Ilkan, M., Magne, V., Tran, M. N., & Suzuki, S. (2018). Acoustic characteristics and learner profiles of low-, mid-and high-level second language fluency. Applied Psycholinguistics, 39(3), 593-617. https://doi.org/10.1017/S0142716417000571
Shadiev, R., Feng, Y., Zhussupova, R., & Altinay, F. (2024). Effects of speech-enabled corrective feedback technology on EFL speaking skills, anxiety and confidence. Computer Assisted Language Learning, 1–37. https://doi.org/10.1080/09588221.2024.2430761.
Sichterman, B., Banihashem, S. K., Verstappen, M., Schipper, M., Waisvisz, P., Noroozi, O., & van Ginkel, S. (2025). Can AI feedback stand alone for fostering students’ presentation performance? A comparison of AI-only versus teacher-supported AI feedback. Interactive Learning Environments, 1-18. https://doi.org/10.1080/10494820.2025.2521338
Sun, W. (2023). The impact of automatic speech recognition technology on second language pronunciation and speaking skills of learners: A mixed methods investigation. Frontiers in Psychology, 14, 1-14. https://doi.org/10.3389/fpsyg.2023.1210187.
Suzuki, S., Kormos, J., & Uchihara, T. (2021). The relationship between utterance and perceived fluency: A meta‐analysis of correlational studies. The Modern Language Journal, 105(2), 435-463. https://doi.org/10.1111/modl.12706
Suzuki, Y., & Hanzawa, K. (2022). Massed task repetition is a double-edged sword for fluency development: An EFL classroom study. Studies in Second Language Acquisition, 44(2), 536-561. https://doi.org/10.1017/S0272263121000358
Szyszka, M., Peltonen, P., & Lintunen, P. (2024). Unravelling the relationship between language anxiety and foreign language speech fluency in a monologue production. Journal of Multilingual and Multicultural Development, 47(1), 1–15. https://doi.org/10.1080/01434632.2024.2387149.
Tavakoli, P., & Skehan, P. (2005). Strategic planning, task structure and performance testing. In R. Ellis (Ed.), Planning and task performance in a second language (pp. 239-273). John Benjamins Publishing Company. https://doi.org/10.1075/lllt.11.15tav.
Tavakoli, P., Nakatsuhara, F., & Hunter, A. (2020). Aspects of fluency across assessed levels of speaking proficiency. The Modern Language Journal, 104(1), 169–191. https://doi.org/10.1111/modl.12620.
Tavakoli, P., & Uchihara, T. (2020). To what extent are multiword sequences associated with oral fluency? Language Learning, 70(2), 506-547. https://doi.org/10.1111/lang.12384
Tisdell, E. J., Merriam, S. B., & Stuckey-Peyrot, H. L. (2025). Qualitative research: A guide to design and implementation. John Wiley & Sons.
Tsai, S. C. (2023). Learning with mobile augmented reality-and automatic speech recognition-based materials for English listening and speaking skills: Effectiveness and perceptions of non-English major English as a foreign language student. Journal of Educational Computing Research, 61(2), 444-465. https://doi.org/10.1177/07356331221111203
Yang, H., Kim, H., Lee, J. H., & Shin, D. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL, 34(3), 327-343. https://doi.org/10.1017/S0958344022000039
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
Zheng, C., Zhang, T., Chen, X., Zhang, H., Wan, J., & Wu, B. (2024). Assessing learners’ English public speaking anxiety with multimodal deep learning technologies. Computer Assisted Language Learning, 1-29. https://doi.org/10.1080/09588221.2024.2351129
Zou, B., Du, Y., Wang, Z., Chen, J., & Zhang, W. (2023). An investigation into artificial intelligence speech evaluation programs with automatic feedback for developing EFL learners’ speaking skills. Sage Open, 13(3), 21582440231193818. https://doi.org/10.1177/21582440231193818
Zou, B., Liviero, S., Ma, Q., Zhang, W., Du, Y., & Xing, P. (2024). Exploring EFL learners’ perceived promise and limitations of using an artificial intelligence speech evaluation system for speaking practice. System, 126, 103497. https://doi.org/10.1016/j.system.2024.103497
