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Improve EFL Students’ Oral English Expression Ability Based on Intelligent Learning Companions: Empirical Research

  • You-You Zhang 1,   
  • Yu Zhao 1,   
  • Si-Yun Chen 2,   
  • Yin-Rong Zhang 3,   
  • Qun-Fang Zeng 1,*

Received: 10 Aug 2025 | Revised: 25 Sep 2025 | Accepted: 12 Oct 2025 | Published: 31 Dec 2025

Abstract

This study focused on the impact of intelligent learning companions (ILC) on Chinese EFL learners’ oral English ability and technology perception. Using a quasi-experimental design, this study selected 51 EFL learners from a university in southeast China and randomly divided them into an experimental group (EG, n = 24) and a control group (CG, n = 27). During the 16-week intervention period, the experimental group adopted the intelligent learning companion teaching strategy supported by artificial intelligence technology (Relying on the IFlytek Spark Platform), while the control group adopted the learning companion strategy guided by teachers. The experimental group not only had better oral English scores (p = 0.001 < 0.05) but also showed a significant increase in technology perception (p = 0.01 < 0.05). The research provides a new perspective for effectively integrating artificial intelligence technology in oral English teaching. Also, it offers strong empirical support for theoretical study and practical application in enhancing the oral expression ability of the second language, English.

References 

  • 1.

    Alvarez, J., & Lane, S. (2023). Rising against the machine: Appeasing the educators’ fears of artificial intelligence taking over foreign language education. UNC System Learning and Technology Journal, 1(1). https://journals.charlotte.edu/ltj.

  • 2.

    Aprin, F., Peters, P., & Hoppe, H. U. (2024). The effectiveness of a virtual learning companion for supporting the critical judgment of social media content. Education and Information Technologies, 29(10), 12797–12830. https://doi.org/10.1007/s10639-023-12275-6.

  • 3.

    Baker, M. J., & Baker, W. H. (2023). Qualitative oral-presentation feedback: Comparisons from business professionals, instructors, and student peers. Business and Professional Communication Quarterly, 86(1), 5–32. https://doi.org/10.1177/23294906221120015.

  • 4.

    Baksh, F., Zorec, M. B., & Kruusamäe, K. (2024). Open-source robotic study companion with multimodal human–robot interaction to improve the learning experience of university students. Applied Sciences, 14(13), 5644. https://doi.org/10.3390/app14135644.

  • 5.

    Benu, N. N., Beeh, N., & Nenotek, S. A. (2025). Implementing deep learning in the EFL classroom: Strategies for fostering mindful, meaningful, and joyful language learning. Journal of Language, Education, Literature, and Culture, 3(1). https://doi.org/10.33323/l.v3i1.64.

  • 6.

    Chen, Z. H., Hsu, H. L., Huang, C. F., Liao, C. Y., & Chou, C. Y. (2025). Pet-like learning companions: Past research and future directions. Research & Practice in Technology Enhanced Learning, 20, 33. https://doi.org/10.58459/rptel.2025.20033.

  • 7.

    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.

  • 8.

    Guo, X.-R., Liu, S.-Y., Gong, S.-Y., Cao, Y., Wang, J., & Fang, Y. (2024). Promoting math learning in educational games with virtual companions providing learning supports. Education and Information Technologies, 29(16), 22341–22370. https://doi.org/10.1007/s10639-024-12741-9.

  • 9.

    Hammond, R. M. (1988). Accuracy versus communicative competency: The acquisition of grammar in the second language classroom. Hispania, 71(2), 408–417. https://doi.org/10.2307/343089.

  • 10.

    Han, L. (2022). Students’ daily English situational teaching based on virtual reality technology. Mobile Information Systems, 2022(1), 1222501. https://doi.org/10.1155/2022/1222501.

  • 11.

    Hu, Y.-H. (2022). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies, 27(2), 2013–2037. https://doi.org/10.1007/s10639-021-10664-3.

  • 12.

    Huang, M. (2024). Empowering oral proficiency in a large-scale class: Video-recorded oral presentations and mobile-assisted peer assessment in a Chinese middle school. Computer Assisted Language Learning, 37(5–6), 1–21. https://doi.org/10.1080/09588221.2024.2382851.

  • 13.

    Hwang, G.-J., Yang, L.-H., & Wang, S.-Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69, 121–130. https://doi.org/10.1016/j.compedu.2013.07.008.

  • 14.

    Jegede, O. O. (2024). Artificial intelligence and English language learning: Exploring the roles of AI-driven tools in personalizing learning and providing instant feedback. Universal Library of Languages and Literatures, 1(2). https://doi.org/10.70315/uloap.ullli.2024.0102002.

  • 15.

    Jeon, J. (2024). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 37(1–2), 1–26. https://doi.org/10.1080/09588221.2021.2021241.

  • 16.

    Karakuş, İ. (2025). International students’ experiences on speaking and writing skills in language learning processes in higher education. PLoS ONE, 20(8), e0329331. https://doi.org/10.1371/journal.pone.0329331.

  • 17.

    Katsarou, D.V., Mantsos, E., Papadopoulou, S., Sofologi, M., Efthymiou, E., Vasileiou, I., Megari, K., Theodoratou, M., & Kougioumtzis, G.A. (2025). Exploring AI technology in grammar performance testing for children with learning disabilities. Education Sciences, 15(3), 351. https://doi.org/10.3390/educsci15030351.

  • 18.

    Kundu, A., & Bej, T. (2025). Transforming EFL teaching with AI: A systematic review of empirical studies. International Journal of Artificial Intelligence in Education, 35, 2281–2314. https://doi.org/10.1007/s40593-025-00470-0.

  • 19.

    Lai, Z. C. (2025). Enhancing EFL oral proficiency through a ChatGPT-integrated BOPPPS learning framework. International Journal of Online Pedagogy and Course Design (IJOPCD), 15(1), 1–21. https://doi.org/10.4018/IJOPCD.383301.

  • 20.

    Lin, V., Yeh, H. C., Huang, H. H., & Chen, N. S. (2022). Enhancing EFL vocabulary learning with multimodal cues supported by an educational robot and an IoT-based 3D book. System, 104, 102691. https://doi.org/10.1016/j.system.2021.102691.

  • 21.

    Liu, J., Wang, X., & Zhang, J. (2025). Investigating elderly individuals’ acceptance of artificial intelligence (AI)-powered companion robots: The influence of individual characteristics. Behavioral Sciences, 15(5), 697. https://doi.org/10.3390/bs15050697.

  • 22.

    Liu, W. (2013). Role of teachers in oral English teaching. In Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012: Volume 4 (pp. 13–18). Springer London. https://doi.org/10.1007/978-1-4471-4853-1_2.

  • 23.

    Lo, C. K., Yu, P. L. H., Xu, S., Ng, D. T. K., & Jong, M. S. Y. (2024). Exploring the application of ChatGPT in ESL/EFL education and related research issues: A systematic review of empirical studies. Smart Learning Environments, 11(1), 50. https://doi.org/10.1186/s40561-024-00342-5.

  • 24.

    Luquin, M. (2025). Enhancing accuracy through model texts: Long-term effects on EFL children’s oral interaction. In M. d. P. García Mayo (Ed.), Investigating Attention to Form and Individual Differences: Research with EFL Children (pp. 155–183). Springer. https://doi.org/10.1007/978-3-031-80924-8_7.

  • 25.

    Ma, G., & Yang, X. (2025). Fostering intercultural competence through AI driven tools: A case study of linguistic and cultural adaptability in Chinese EFL education. Digital Technologies Research and Applications, 4(1), 158–169. https://doi.org/10.54963/dtra.v4i1.1131.

  • 26.

    Mohamed, A. M. (2024). Exploring the potential of an AI-based chatbot (ChatGPT) in enhancing English as a foreign language (EFL) teaching: Perceptions of EFL faculty members. Education and Information Technologies, 29, 3195–3217. https://doi.org/10.1007/s10639-023-11917-z.

  • 27.

    Morgenstern, A. (2023). Children's multimodal language development from an interactional, usage-based, and cognitive perspective. Wiley Interdisciplinary Reviews: Cognitive Science, 14(2), e1631. https://doi.org/10.1002/wcs.1631.

  • 28.

    Mukherjee, H., & Bhonge, P. (2025). Assessing skew normality in marks distribution: A comparative analysis of Shapiro Wilk tests. arXiv. https://doi.org/10.48550/arXiv.2501.14845.

  • 29.

    Ngo, T. T. N., Chen, H. H. J., & Lai, K. K. W. (2024). The effectiveness of automatic speech recognition in ESL/EFL pronunciation: A meta-analysis. ReCALL, 36(1), 4–21. https://doi.org/10.1017/S0958344023000113.

  • 30.

    Qian, J., Jiang, X., Ma, J., Li, J., Gao, Z., & Qin, X. (2023). Accompany children’s learning for you: An intelligent companion learning system. Computer Graphics Forum, 42, e14862. https://doi.org/10.1111/cgf.14862.

  • 31.

    Ranjbar, M., Amirian, S. M. R., & Vaghayei, F. (2025). A qualitative analysis of the effect of group oral presentation and peer assessment on EFL learners’ self-efficacy. Current Psychology, 44, 3747–3759. https://doi.org/10.1007/s12144-025-07410-0.

  • 32.

    Rong, M., Yao, Y., Li, Q., & Chen, X. (2025). Exploring student engagement with artificial intelligence-guided chatbot feedback in EFL writing: Interactions and revisions. Computer Assisted Language Learning, 1–30. https://doi.org/10.1080/09588221.2025.2539979.

  • 33.

    Shi, J., Sitthiworachart, J., & Hong, J. C. (2024). Supporting project-based learning for students’ oral English skill and engagement with immersive virtual reality. Education and Information Technologies, 29(11), 14127–14150. https://doi.org/10.1007/s10639-023-12433-w.

  • 34.

    Sun, L. (2025). Enhancing intercultural competence of Chinese English majors through AI-enabled collaborative online international learning (COIL) in the digital era. Education and Information Technologies, 30, 7995–8027. https://doi.org/10.1007/s10639-024-13143-7.

  • 35.

    Sun, X., Dou, W., & Yang, Y. (2025). The socio-emotional dangers of using artificial intelligence (AI) technologies in second language (L2) education: Unveiling Chinese EFL teachers' perceptions and experiences. Acta Psychologica, 261, 105956. https://doi.org/10.1016/j.actpsy.2025.105956.

  • 36.

    Tiandem-Adamou, Y. (2024). Using generative artificial intelligence to support EFL students’ writing proficiency in university in China. Journal of Educational Technology and Innovation, 6(4), 213–235. https://doi.org/10.61414/jeti.v6i4.213.

  • 37.

    Wang, W., Rezaei, Y. M., & Izadpanah, S. (2024). Speaking accuracy and fluency among EFL learners: The role of creative thinking, emotional intelligence, and academic enthusiasm. Heliyon, 10(18), e37620. https://doi.org/10.1016/j.heliyon.2024.e37620.

  • 38.

    Wang, X. (2025). Oral English pronunciation evaluation system based on PID algorithm. Procedia Computer Science, 261, 1043–1049. https://doi.org/10.1016/j.procs.2025.04.683.

  • 39.

    Wei, W., Zhao, A., & Ma, H. (2025). Understanding how AI chatbots influence EFL learners’ oral English learning motivation and outcomes: Evidence from Chinese learners. IEEE Access, 13, 56699–56716. https://doi.org/10.1109/ACCESS.2025.3478921.

  • 40.

    Yang, G., Rong, Y. D., Wang, Y. L., Zhang, Y. Y., Yan, J. J., & Tu, Y. F. (2025). How generative artificial intelligence supported reflective strategies promote middle school students’ conceptual knowledge learning: An empirical study from China. Interactive Learning Environments, 1–26. https://doi.org/10.1080/10494820.2025.2521339.

  • 41.

    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.

  • 42.

    Zhang, J. (2025). Integrating chatbot technology into English language learning to enhance student engagement and interactive communication skills. Journal of Computational Methods in Sciences and Engineering, 25(3), 2288–2299. https://doi.org/10.1177/14727978241312992.

  • 43.

    Zhao, C. (2024). AI-assisted assessment in higher education: A systematic review. Journal of Educational Technology and Innovation, 6(4), 209–232. https://doi.org/10.61414/jeti.v6i4.209.

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DOI
10.61414/7sbbzt07
Issue
Volume 7, Issue 4
How to Cite
Zhang, Y.-Y.; Zhao, Y.; Chen, S.-Y.; Zhang, Y.-R.; Zeng, Q.-F. Improve EFL Students’ Oral English Expression Ability Based on Intelligent Learning Companions: Empirical Research. Journal of Educational Technology and Innovation 2025, 7 (4), 73–86. https://doi.org/10.61414/7sbbzt07.
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