Abstract
Personalized AI-driven platforms, such as ChatGPT, offer individualized guidance that aligns with the specific needs of each learner, potentially boosting motivation, fostering independence, and improving knowledge retention. This study investigates the impact of a personized AI-driven language learning platform, utilizing ChatGPT, on EFL learners' perceived adaptability, autonomy in their learning processes, and retention of language skills. Sixty intermediate-level female students from Riyadh, Saudi Arabia, were randomly divided into two groups: an experimental group (n = 30) that engaged in adaptive AI-enhanced instruction, and a control group (n = 30) that experienced static, non-personalized digital content. The research utilized a quantitative pre-post experimental framework, featuring pre- and posttests to evaluate language retention, a validated questionnaire to measure learner autonomy, and a scale to assess perceived adaptability. Quantitative findings examined via Analysis of Covariance (ANCOVA) revealed significant post-intervention enhancements across all three variables for the experimental group, with considerable effect sizes observed for perceived adaptability and language retention, as well as a moderate effect for autonomy. The results highlight the crucial role of personalized AI in fostering independent, adaptable, and retention-boosting language learning experiences. The findings of this study indicate significant factors to consider when integrating intelligent tutoring systems into EFL instruction and improving learner-centered digital education. This encompasses the essential need for educator training in AI prompt engineering, the creation of curricula that incorporate decision-making opportunities, and the emphasis on policies that endorse adaptive learning platforms.
References
Al-Abdullatif, A. M., & Alsubaie, M. A. (2022). Using digital learning platforms for teaching Arabic literacy: A post-pandemic mobile learning scenario in Saudi Arabia. Sustainability, 14(19), 11868. https://doi.org/10.3390/su141911868
Alshaikhi, T., & Khasawneh, M. A. (2024). Enhancing teacher competence in differentiated instruction for English language learners with disabilities: A professional development intervention. World Journal of English Language, 15(1), 101. https://doi.org/10.5430/wjel.v15n1p101
Alharbi, K. (2024). Enhancing technical vocabulary acquisition: a multimedia strategic approach for English for specific purposes during COVID-19. Interactive Learning Environments, 32(9), 4812-4830. https://doi.org/10.1080/10494820.2023.2205902
Alhawsawi, S. (2014). Investigating student experiences of learning English as a foreign language in a preparatory programme in a Saudi university [Doctoral dissertation, University of Sussex]. University of Sussex Research Repository.
Alonazi, S. M. (2017). The Role of Teachers in Promoting Learner Autonomy in Secondary Schools in Saudi Arabia. English Language Teaching, 10(7), 183-202.
Alrabai, F. (2017). From teacher dependency to learner independence: Saudi learners’ perceptions of learner autonomy in English language learning. Learning and Teaching in Higher Education: Gulf Perspectives, 14(1). https://doi.org/10.18538/lthe.v14.n1.262
Alsaywid, B. S., Alajlan, S. A., & Lytras, M. D. (2023). Transformative learning as a bold strategy for the vision 2030 in Saudi Arabia: Moving higher healthcare education forward. In Technology-enhanced healthcare education: Transformative learning for patient-centric health (pp. 187-207). Emerald Publishing Limited.
Alshathry, S., & Alojail, M. (2024). Evaluating post-pandemic undergraduate student satisfaction with online learning in Saudi Arabia: The significance of self-directed learning. Applied Sciences, 14(19), 8889. https://doi.org/10.3390/app14198889
Asadi, M., Ebadi, S., & Mohammadi, L. (2025). The impact of integrating ChatGPT with teachers’ feedback on EFL writing skills. Thinking Skills and Creativity, 56, 101766. https://doi.org/10.1016/j.tsc.2025.101766
Asiri, J., & Shukri, N. (2020). Preparatory learners’ perspectives of learner autonomy in the Saudi context. Arab World English Journal, 11(2), 94–113. https://doi.org/10.24093/awej/vol11no2.8
Aslan, S., Alyuz, N., Tanriover, C., Mete, S. E., Okur, E., D'Mello, S. K., & Arslan Esme, A. (2019, May). Investigating the impact of a real-time, multimodal student engagement analytics technology in authentic classrooms. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-12).
Benson, P. (2013). Teaching and researching: Autonomy in language learning. Routledge.
Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning: A temporal ridgeline of optimal retention. Psychological Science, 19(11), 1095-1102. https://doi.org/10.1111/j.1467-9280.2008.02209.x
Chang, W. L., & Sun, J. C. Y. (2024). Evaluating AI's impact on self-regulated language learning: A systematic review. System, 126, 103484. https://doi.org/10.1016/j.system.2024.103484
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Ding, D., & Yusof, M. B. (2025). Investigating the role of AI-powered conversation bots in enhancing L2 speaking skills and reducing speaking anxiety: a mixed methods study. Humanities and Social Sciences Communications, 12(1), 1-16.
Dizon, G. (2024). ChatGPT as a tool for self-directed foreign language learning. Innovation in Language Learning and Teaching, 1-17. https://doi.org/10.1080/17501229.2024.2413406
Dornyei, Z. (2013). The psychology of second language acquisition. Oxford University Press.
Dumitru, C. (2024). Future of learning: Adaptive learning systems based on language generative models in higher education. In Impact of artificial intelligence on society (pp. 31-42). Chapman and Hall/CRC.
Elmaadaway, M. A. N., El‐Naggar, M. E., & Abouhashesh, M. R. I. (2025). Improving Primary School Students' Oral Reading Fluency Through Voice Chatbot‐Based AI. Journal of Computer Assisted Learning, 41(2), e70019. https://doi.org/10.1111/jcal.70019
Essa, S. G., Celik, T., & Human-Hendricks, N. E. (2023). Personalized adaptive learning technologies based on machine learning techniques to identify learning styles: A systematic literature review. IEEE Access, 11, 48392-48409. https://doi.org/10.1109/ACCESS.2023.3276439
Francescucci, A., & Foster, M. (2013). The VIRI (virtual, interactive, real-time, instructor-led) classroom: The impact of blended synchronous online courses on student performance, engagement, and satisfaction. Canadian Journal of Higher Education, 43(3), 78-91.
Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning & Technology, 26(2), 5–24. http://hdl.handle.net/10125/73474
Gronier, G., & Baudet, A. (2021). Psychometric evaluation of the F-SUS: creation and validation of the French version of the system usability scale. International Journal of Human–Computer Interaction, 37(16), 1571-1582. https://doi.org/10.1080/10447318.2021.1898828
Gulnaz, F., Alfaqih, A. M., & Mashhour, N. A. (2015). Paradigm shift: a critical appraisal of traditional and innovative roles of an English teacher in Saudi ELT classrooms. Theory and Practice in Language Studies, 5(5), 934-946. http://dx.doi.org/10.17507/tpls.0505.07
He, X. S., & Loewen, S. (2022). Stimulating learner engagement in app-based L2 vocabulary self-study: Goals and feedback for effective L2 pedagogy. System, 105, 102719. https://doi.org/10.1016/j.system.2021.102719
Hossain, M. K., & Al Younus, M. A. (2025). Teachers’ perspectives on integrating ChatGPT into EFL writing instruction. TESOL Communications, 4(1), 41-60. https://doi.org/10.58304/tc.20250103
Iku-Silan, A., Hwang, G. J., & Chen, C. H. (2023). Decision-guided chatbots and cognitive styles in interdisciplinary learning. Computers & Education, 201, 104812. https://doi.org/10.1016/j.compedu.2023.104812
Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research landscape of adaptive learning in education: A bibliometric study on research publications from 2000 to 2022. Sustainability, 15(4), 3115. https://doi.org/10.3390/su15043115
Jumah, J., Jumah, O., & Hassan, E. (2024, November). The Impact of Artificial Intelligence on Flipped Learning and Online Learning. In International Conference on Technology and Innovation in Learning, Teaching and Education (pp. 93-105). Springer Nature Switzerland.
Kessler, M., Loewen, S., & Gönülal, T. (2025). Mobile-assisted language learning with Babbel and Duolingo: comparing L2 learning gains and user experience. Computer Assisted Language Learning, 38(4), 690-714. https://doi.org/10.1080/09588221.2023.2215294
Kim, J., Kim, H., Kim, J. H., Noh, S., & Park, J. H. (2024, September). Analysis of the current status and policies of elementary AI and digital education in South Korea. In 2024 4th International Conference on Educational Technology (ICET) (pp. 245-248). IEEE.
Klimova, B., Pikhart, M., Polakova, P., Cerna, M., Yayilgan, S. Y., & Shaikh, S. (2023). A systematic review on the use of emerging technologies in teaching English as an applied language at the university level. Systems, 11(1), 42. https://doi.org/10.3390/systems11010042
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. Relc Journal, 54(2), 537-550. https://doi.org/10.1177/00336882231162868
Koutsantonis, D., Koutsantonis, K., Bakas, N. P., Plevris, V., Langousis, A., & Chatzichristofis, S. A. (2022). Bibliometric literature review of adaptive learning systems. Sustainability, 14(19), 12684. https://doi.org/10.3390/su141912684
Kukulska-Hulme, A., & Ilic, P. (2025). MALL in the age of AI. In The Palgrave encyclopedia of computer-assisted language learning (pp. 1–8). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-50028-2_17
Kwak, M., Jenkins, J., & Kim, J. (2023). Adaptive programming language learning system based on generative AI. Issues in Information Systems, 24(3), 222-231. https://doi.org/10.48009/3_iis_2023_119
Lai, Y., Saab, N., & Admiraal, W. (2022). Learning strategies in self-directed language learning using mobile technology in higher education: A systematic scoping review. Education and Information Technologies, 27(6), 7749-7780. https://doi.org/10.1007/s10639-022-10945-5
Law, V., Ge, X., & Huang, K. (2020). Understanding learners’ challenges and scaffolding their ill-structured problem solving in a technology-supported self-regulated learning environment. In Handbook of Research in Educational Communications and Technology: Learning Design (pp. 321-343). Springer International Publishing.
Lee, S., Jeon, J., McKinley, J., & Rose, H. (2025). Generative AI and English language teaching: A global Englishes perspective. Annual Review of Applied Linguistics, 1-24. https://doi.org/10.1017/S0267190525100184
Leshchenko, M., Lavrysh, Y., Halatsyn, K., Feshchuk, A., & Prykhodko, D. (2023). Technology-enhanced personalized language learning: Strategies and challenges. International Journal of Enmerging Technologies in Learning, 13(18), 120-136.
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
Luan, L., Lin, X., Dai, Y., Allen, L., & Hu, S. (2024). Transforming STEM education: Leveraging ChatGPT for enhanced learning in psychology and neuroscience. In X. Lin, R. Y. Chan, S. Sharma, & K. Bista (Eds.), ChatGPT and global higher education: Using artificial intelligence in teaching and learning (pp. 100–121). STAR Scholars Press.
Luo, L., & Yusuf, A. (2025). Bridging AI and pedagogy: how AI-adaptive feedback shapes Chinese EFL students’ writing engagement, metacognitive writing strategies, and writing performance. Assessment & Evaluation in Higher Education, 1-24. https://doi.org/10.1080/02602938.2025.2548919
Luo, R. Z., & Zhou, Y. L. (2024). The effectiveness of self‐regulated learning strategies in higher education blended learning: A five years systematic review. Journal of Computer Assisted Learning, 40(6), 3005-3029. https://doi.org/10.1111/jcal.13052
McLaughlin, B. (1987). Theories of second-language learning. Edward Arnold.
Meniado, J. C. (2023). The impact of ChatGPT on English language teaching, learning, and assessment: A rapid review of literature. Arab World English Journal, 14(3), 1-13. https://doi.org/10.24093/awej/vol14no3.1
Namaziandost, E., & Rezai, A. (2024). Interplay of academic emotion regulation, academic mindfulness, L2 learning experience, academic motivation, and learner autonomy in intelligent computer-assisted language learning: A study of EFL learners. System, 125, 103419–103419. https://doi.org/10.1016/j.system.2024.103419
Nation, P. (2007). The four strands. International Journal of Foreign Language Teaching, 3(2), 7–20.
Phumeechanya, N., & Wannapiroon, P. (2014). Design of problem-based with scaffolding learning activities in ubiquitous learning environment to develop problem-solving skills. Procedia-Social and Behavioral Sciences, 116, 4803-4808. https://doi.org/10.1016/j.sbspro.2014.01.1028
Polakova, P., & Klimova, B. (2024). Implementation of AI-driven technology into education–a pilot study on the use of chatbots in foreign language learning. Cogent Education, 11(1), 2355385. https://doi.org/10.1080/2331186X.2024.2355385
Qiao, H., & Zhao, A. (2023). Artificial intelligence-based language learning: illuminating the impact on speaking skills and self-regulation in Chinese EFL context. Frontiers in psychology, 14, 1255594. https://doi.org/10.3389/fpsyg.2023.1255594
Reinders, H., & White, C. (2016). 20 years of autonomy and technology: How far have we come and where to next? Language Learning & Technology, 20(2), 143–155.
Ross, E. A. (2023). A new frontier: AI and ancient language pedagogy. Journal of Classics Teaching, 24(48), 143-161. https://doi.org/10.1017/S2058631023000430
Schmidt, T., & Strasser, T. (2022). Artificial intelligence in foreign language learning and teaching: a CALL for intelligent practice. Anglistik: International Journal of English Studies, 33(1), 165-184.
Schmitt, N., & Schmitt, D. (2020). Vocabulary in language teaching. Cambridge University Press.
Sharma, S., Mittal, P., Kumar, M., & Bhardwaj, V. (2025). The role of large language models in personalized learning: a systematic review of educational impact. Discover Sustainability, 6(1), 1-24. https://doi.org/10.1007/s43621-025-01094-z
Spratt, M., Humphreys, G., & Chan, V. (2002). Autonomy and motivation: Which comes first?. Language Teaching Research, 6(3), 245-266. https://doi.org/10.1191/1362168802lr106oa
Stokel-Walker, C. (2024). How AI ate the world: A brief history of artificial intelligence–and its long future. Canbury Press.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8
Tai, T. Y., & Chen, H. H. J. (2024). Improving elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions. Computers & Education, 220, 105112. https://doi.org/10.1016/j.compedu.2024.105112
Tian, Z., & Wang, C. (Eds.). (2025). Rethinking Language Education in the Age of Generative AI. Taylor & Francis Group.
Tram, N. H. M. (2025). Unveiling the drivers of AI integration among language teachers: Integrating UTAUT and AI-TPACK. Computers in the Schools, 42(2), 100-120. https://doi.org/10.1080/07380569.2024.2441155
Tubman, C. (2012). Adaptive learning environments: Foundations and frontiers (Vol. 85). Springer Science & Business Media.
VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rosé, C. P. (2007). When are tutorial dialogues more effective than reading?. Cognitive Science, 31(1), 3-62. https://doi.org/10.1080/03640210709336984
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Wu, T. T., Hapsari, I. P., & Huang, Y. M. (2025). Effects of incorporating AI chatbots into think–pair–share activities on EFL speaking anxiety, language enjoyment, and speaking performance. Computer Assisted Language Learning, 1-39. https://doi.org/10.1080/09588221.2025.2478271
Xia, J., Ge, Y., Shen, Z., & Rahman Najar, M. (2024). The auxiliary role of artificial intelligence applications in mitigating the linguistic, psychological, and educational challenges of teaching and learning chinese language by non-chinese students. International Review of Research in Open and Distributed Learning, 25(3), 116-133. https://doi.org/10.19173/irrodl.v25i3.7680
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