Predicting Metacognitive Engagement in EFL Writing: The Role of Technology Acceptance and Self-regulated Learning
Country:
(1) Department of English Language Education, Universitas Tanjungpura, Indonesia
(2) Department of Mandarin Language Education, Universitas Tanjungpura, Indonesia
(3) Department of Indonesian Language Education, Universitas Tanjungpura, Indonesia
(4) Guangxi Minzu University, China
This study aims to provide insight and a deeper understanding of how technology acceptance contributes to the development of metacognitive engagement in EFL students by integrating the self-regulatory learning process. This study employed a correlational survey design. The data were collected using a questionnaire based on the Technology Acceptance Model (TAM) and Self-Regulated Learning (SRL), both of which are previously validated. Data analysis was performed using descriptive statistics and Partial Least Squares-Structural Equation Modeling (PLS-SEM). In this model, metacognitive engagement was represented as a second-order construct comprising the SRL dimensions. Model evaluation included testing the measurement model (construct validity and reliability) and the structural model to examine the influence of perceived usefulness and ease of use on metacognitive engagement in AI-assisted writing. The findings revealed that the proposed structural model demonstrated substantial predictive power, with Perceived Usefulness and Perceived Ease of Use jointly explaining 64.7% of the variance in EFL students’ metacognitive engagement in AI-assisted writing (R² = 0.647). Perceived Usefulness (β = 0.498, t = 9.132, p < .001) and Perceived Ease of Use (β = 0.386, t = 6.421, p < .001) had significant positive effects on metacognitive engagement. These results confirmed the H1 and H2 hypotheses proposed in this study, indicating that technology acceptance is significantly associated with self-regulated learning processes underlying metacognitive engagement, thereby providing empirical support for H3. The findings demonstrate that technology acceptance significantly enhances metacognitive engagement in AI-assisted EFL writing. This suggests that AI tools play a meaningful role in fostering reflective, self-regulated writing practices among EFL learners.
Keywords: AI-assisted writing, technology acceptance, self-regulated learning, metacognitive engagement, PLS-SEM.
Aladini, A., Ismail, S. M., Khasawneh, M. A. S., & Shakibaei, G. (2024). Self-directed writing development across computer/AI-based tasks: Unraveling the traces on L2 writing outcomes, growth mindfulness, and grammatical knowledge. Computers in Human Behavior Reports, 17(March), 100566. https://doi.org/10.1016/j.chbr.2024.100566
Alangari, T. S. (2025). The effect of AI-assisted learning on EFL writing proficiency: Quasi-experimental and cluster analysis. Educational Process International Journal, 17(1), e2025345. https://doi.org/10.22521/edupij.2025.17.345
Amani, N., & Bisriyah, M. (2025). University Students’ Perceptions of AI-Assisted Writing Tools in Supporting Self-Regulated Writing Practices. IJELTAL (Indonesian Journal of English Language Teaching and Applied Linguistics), 10(1), 91. https://doi.org/10.21093/ijeltal.v10i1.1942
Braad, E., Degens, N., Barendregt, W., & IJsselsteijn, W. A. (2022). Improving metacognition through self-explication in a digital self-regulated learning tool. Educational Technology Research and Development, 70(6), 2063–2090. https://doi.org/10.1007/s11423-022-10156-2
Brenner, C. A. (2022). Self-regulated learning, self-determination theory and teacher candidates’ development of competency-based teaching practices. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-021-00184-5
Campos, M. (2025). AI-assisted feedback in CLIL courses as a self-regulated language learning mechanism: Students ’ perceptions and experiences. European Public & Social Innovation Review, 10, 1–14. https://doi.org/10.31637/epsir-2025-1568
Chen, C., & Gong, Y. (2025). The role of AI-assisted learning in academic writing: A mixed-methods study on Chinese as a second language students. Education Sciences, 15(2), 141. https://doi.org/10.3390/educsci15020141
Chung, H. Q., Chen, V., & Olson, C. B. (2021). The impact of self-assessment, planning and goal setting, and reflection before and after revision on student self-efficacy and writing performance. Reading and Writing, 34(7), 1885–1913. https://doi.org/10.1007/s11145-021-10186-x
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (Eighth). Routledge Taylor & Francis Group.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative,and mixed-methods approaches. SAGE Publication, Inc. https://doi.org/10.4324/9780429469237-3
Dahri, N. A., Yahaya, N., Al-rahmi, W. M., & Aldraiweesh, A. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education : A mixed-methods study. Heliyon, 10(8), e29317. https://doi.org/10.1016/j.heliyon.2024.e29317
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8), e29317. https://doi.org/10.1016/j.heliyon.2024.e29317
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71(March). https://doi.org/10.1016/j.ijinfomgt.2023.102642
Elsa, E., Prihantoro, P., & Wu, T. (2025). AI-assisted self-regulated strategy development: A pathway to strengthening L2 writing self-efficacy. In Lecture Notes in Computer Science (pp. 320–329). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-98197-5_34
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2024). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Fauzi, C., Denashurya, N. I., & Rahmani, E. F. (2025). ChatGPT in academic learning: EFL senior college students’ attitudes and gender differences toward ChatGPT. The Journal of English Teaching for Young and Adult Learners, 4(2), 44–55.
Feng, Z. (2023). Metacognitive strategies, automated writing evaluation feedback and English writing performance of Chinese EFL college students. International Journal of Education and Humanities, 11(3), 483–486. https://doi.org/10.54097/ijeh.v11i3.15154
García, P., Maghanoy, D. R. G., Ahmad, F. J., Manabat, A. D., & Arnoco, J. P. P. (2025). ChatGPT, Grammarly, and Quillbot: Perceptions of students and teachers towards the use of AI tools in writing. Journal of English Language Teaching and Applied Linguistics, 7(3), 161–167. https://doi.org/10.32996/jeltal.2025.7.3.16
Godsk, M., & Møller, K. L. (2024). Engaging students in higher education with educational technology. Education and Information Technologies, 30(3), 2941–2976. https://doi.org/10.1007/s10639-024-12901-x
Granström, M., & Oppi, P. (2025). Student engagement with AI tools in learning: evidence from a large-scale Estonian survey. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1688092
Guo, K., Wang, J., & Chu, S. K. W. (2022). Using chatbots to scaffold EFL students’ argumentative writing. Assessing Writing, 54(2). https://doi.org/10.1016/j.asw.2022.100666
He, T.-H., Chang, S., & Chen, S.-H. E. (2011). Multiple Goals, Writing Strategies, and Written Outcomes for College Students Learning English as a Second Language. Perceptual and Motor Skills, 112(2), 401–416. https://doi.org/10.2466/11.21.28.pms.112.2.401-416
Jin, Y., Yang, K., Martinez-Maldonado, R., Gašević, D., & Yan, L. (2025). The agency gap: How generative AI Literacy Shapes Independent Writing after AI Support. https://doi.org/10.48550/ARXIV.2507.04398
Jin, Y., Yang, K., Martínez‐Maldonado, R., Gašević, D., & Yan, L. (2025). Do students write better post-AI support? Effects of Generative AI literacy and chatbot interaction strategies on multimodal academic writing. In arXiv (Cornell University) (pp. 1–22). Cornell University. https://doi.org/10.48550/arxiv.2507.04398
Johnston, H., Wells, R., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20(1). https://doi.org/10.1007/s40979-024-00149-4
Kanont, K., Pingmuang, P., Simasathien, T., Wisnuwong, S., Wiwatsiripong, B., Poonpirome, K., Songkram, N., & Khlaisang, J. (2024). Generative-AI, a learning assistant? Factors influencing higher-ed students’ technology acceptance. The Electronic Journal of E-Learning, 22(6), 18–33. https://doi.org/10.34190/ejel.22.6.3196
Koltovskaia, S., Rahmati, P., & Saeli, H. (2024). Graduate students’ use of ChatGPT for academic text revision: Behavioral, cognitive, and affective engagement. Journal of Second Language Writing, 65, 101130. https://doi.org/10.1016/j.jslw.2024.101130
Kong, S. C., Lee, J. C., & Tsang, O. (2024). A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking. Research and Practice in Technology Enhanced Learning, 19, 30. https://doi.org/10.58459/rptel.2024.19030
Lai, Z. C.-C. (2025). The impact of AI-assisted blended learning on writing efficacy and resilience. International Journal of Computer-Assisted Language Learning and Teaching, 15(1), 1–21. https://doi.org/10.4018/ijcallt.377174
Lan, M., & Zhou, X. (2025). A qualitative systematic review on AI empowered self-regulated learning in higher education. Npj Science of Learning, 10(1), 21. https://doi.org/10.1038/s41539-025-00319-0
Lee, W., & Ng, S. (2009). Reducing student reticence through teacher interaction strategy. ELT Journal, 64(3). https://doi.org/10.1093/elt/ccp080
Li-jie, H., Yusoff, S. M., & Marzaini, A. F. M. (2024). Influence of AI-driven educational tools on critical thinking dispositions among university students in Malaysia: a study of key factors and correlations. Education and Information Technologies, 30(6), 8029–8053. https://doi.org/10.1007/s10639-024-13150-8
Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2). https://doi.org/10.1080/2331186X.2023.2236469
Mazari, N. (2025). Building metacognitive skills using AI tools to help higher education students reflect on their learning process. RHS-Revista Humanismo y Sociedad, 13(1). https://doi.org/10.22209/rhs.v13n1a04
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(4), 3–18. https://doi.org/10.24093/awej/vol14no4.1
Mohammed, S. J., & Khalid, M. W. (2025). Under the world of AI-generated feedback on writing: mirroring motivation, foreign language peace of mind, trait emotional intelligence, and writing development. Language Testing in Asia, 15(1). https://doi.org/10.1186/s40468-025-00343-2
Mouloudj, F., Aljaeed, S. A., & Asanza, D. M. (2025). Understanding students’ intentions to use AI for English language learning. In Advances in Computational Intelligence and Robotics book series (pp. 157–178). IGI Global. https://doi.org/10.4018/979-8-3693-9077-1.ch007
Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 49(5), 847–864. https://doi.org/10.1080/03075079.2024.2323593
Nhan, L. K., Hoà, N. T., & Quang, L. V. N. (2025). Leveraging AI for writing instruction in EFL classrooms: Opportunities and challenges. Educational Process International Journal, 15(1), e2025158. https://doi.org/10.22521/edupij.2025.15.158
Ni, A., & Cheung, A. (2022). Understanding secondary students’ continuance intention to adopt AI-powered intelligent tutoring system for English learning. Education and Information Technologies, 28(3), 3191–3216. https://doi.org/10.1007/s10639-022-11305-z
Philippakos, Z. A. T. (2020). Developing strategic learners: Supporting self-efficacy through goal setting and reflection. The Language and Literacy Spectrum, 30(1), 1. http://files.eric.ed.gov/fulltext/EJ1263260.pdf
Rahayu, R., Weda, S., MULIATI, M., & Vega, N. De. (2024). Artificial Intelligence in writing instruction: A self-determination theory perspective. XLinguae, 17(1), 234–244. https://doi.org/10.18355/xl.2024.17.01.16
Rahmani, E. F. (2023). Undergraduate students‘ perceptions on Quillbot paraphrasing tool. Scripta : English Department Journal, 10(2), 182–190. https://doi.org/10.37729/scripta.v10i2.3674
Rahmayanti, S., Ivone, F. M., Tresnadewi, S., & Nomnian, S. (2025). EFL postgraduate students’ adoption and perceptions of chatbot-assisted academic writing. JEES (Journal of English Educators Society), 10(1), 1–14. https://doi.org/10.21070/jees.v10i1.1894
Rofikah, U., Arafah, B., Nasmilah, N., & Harewaty. (2025). Indonesian students’ perception of AI-assisted EFL academic writing. In Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research (pp. 410–419). https://doi.org/10.2991/978-2-38476-394-8_46
Salam, U. (2024). The integration of ChatGPT in English for foreign language course: Elevating AI writing assistant acceptance. Computers in the Schools, 42(2), 145–165. https://doi.org/10.1080/07380569.2024.2446239
Sarı, E., & Han, T. (2024). The impact of automated writing evaluation on English as a foreign language learners’ writing self‐efficacy, self‐regulation, anxiety, and performance. Journal of Computer Assisted Learning, 40(5), 2065–2080. https://doi.org/10.1111/jcal.13004
Siddiqui, M. N., Feliciano, V. A., Pea, R., & Subramonyam, H. (2025). AI in the Writing Process: How Purposeful AI Support Fosters Student Writing. In Lecture Notes in Computer Science (pp. 190–203). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-98459-4_14
Tabari, M. A. (2021). Task preparedness and L2 written production: Investigating effects of planning modes on L2 learners’ focus of attention and output. Journal of Second Language Writing, 52(June), 100814. https://doi.org/10.1016/j.jslw.2021.100814
Teng, M. F. (2024). “ChatGPT is the companion, not enemies”: EFL learners ’ perceptions and experiences in using ChatGPT for feedback in writing. Computers and Education: Artificial Intelligence, 7(May), 100270. https://doi.org/10.1016/j.caeai.2024.100270
Teng, M. F. (2025). Understanding EFL student writers’ metacognitive awareness in utilizing ChatGPT. System, 135(December), 103848. https://doi.org/10.1016/j.system.2025.103848
Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & de Visser, E. J. (2024). Student perceptions of ChatGPT use in a college essay assignment: Implications for learning, grading, and trust in artificial intelligence. IEEE Transactions on Learning Technologies, 17, 1069–1081. https://doi.org/10.1109/tlt.2024.3355015
Vaughan, G. (2022). Metacognition and Self-Regulated Learning. Opus et Educatio, 9(2). https://doi.org/10.3311/ope.501
Wang, C. (2024). Exploring students’ generative AI-assisted writing processes: Perceptions and experiences from native and nonnative English speakers. Technology Knowledge and Learning, 30(May), 1825–1846. https://doi.org/10.1007/s10758-024-09744-3
Wang, C., Li, Z., & Bonk, C. J. (2024). Understanding self-directed learning in AI-Assisted writing: A mixed methods study of postsecondary learners. Computers and Education Artificial Intelligence, 6, 100247. https://doi.org/10.1016/j.caeai.2024.100247
Wang, C., & Searsmith, D. (2025). Writing With Machines and Peers: Designing for Critical Engagement with Generative AI. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2511.15750
Wang, Y., & Chuang, Y.-W. (2023). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785–4808. https://doi.org/10.1007/s10639-023-12015-w
Wei, P., Wang, X., & Dong, H. (2023). The impact of automated writing evaluation on second language writing skills of Chinese EFL learners: a randomized controlled trial. Frontiers in Psychology, 14(September), 1–11. https://doi.org/10.3389/fpsyg.2023.1249991
Wu, X.-Y., & Chiu, T. K. F. (2025). Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: a configurational analysis. Journal of New Approaches in Educational Research, 14(1). https://doi.org/10.1007/s44322-025-00028-x
Xu, T., & Jumaat, N. F. (2025). Enhancing critical thinking in EFL writing through an AI-supported blended learning model. International Journal of Academic Research in Progressive Education and Development, 14(1). https://doi.org/10.6007/ijarped/v14-i1/24850
Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self‐regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13599
Xu, Z. (2025). Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing. 1–21. http://arxiv.org/abs/2502.00632
Yan, D., & Zhang, S. (2024). L2 writer engagement with automated written corrective feedback provided by ChatGPT: A mixed-method multiple case study. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03543-y
Yang-xi, H., Zhao, S., & Ng, L. L. (2021). How technology tools impact writing performance, lexical complexity, and perceived self-regulated learning strategies in EFL academic writing: A comparative study. Frontiers in Psychology, 12(November), 1–18. https://doi.org/10.3389/fpsyg.2021.752793
Yang, K., Raković, M., Liang, Z., Yan, L., Zeng, Z., Fan, Y., Gašević, D., & Chen, G. (2025). Modifying AI, enhancing essays: How active engagement with generative AI boosts writing quality. LAK ’25: Proceedings of the 15th International Learning Analytics and Knowledge Conference, 568–578. https://doi.org/10.1145/3706468.3706544
Yao, L., & Liu, Y. (2025). Emotional multifaceted feedback on AI tool use in EFL learning initiation: Chain-mediated effects of motivation and metacognitive strategies in an optimized TAM model. In Research Square (pp. 1–14). Research Square (United States). https://doi.org/10.21203/rs.3.rs-6289643/v1
Yin, X., & Dou, K. (2025). An AI-assisted critical thinking intervention to enhance undergraduate EFL learners’ writing proficiency. Studies In Educational Evaluation, 86, 101480. https://doi.org/10.1016/j.stueduc.2025.101480
Zhai, Y., & Nezakatgoo, B. (2025). Evaluating AI-powered applications or enhancing undergraduate students’ metacognitive strategies, self-determined motivation, and social learning in English language education. Scientific Reports, 15(1), 35199. https://doi.org/10.1038/s41598-025-19118-z
Zhao, Z., An, Q., & Liu, J. (2025). Exploring AI tool adoption in higher education: evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1619391
Zheng, Y., Wang, Y., Liu, K. S., & Jiang, M. Y. (2024). Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning. Education and Information Technologies, 29(17), 23547–23575. https://doi.org/10.1007/s10639-024-12763-3
Zimmerman, B. J. (2000). Attaining self-regulation : A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), handbook of self-Regulation. Cambridge, MA : Academic Press, 13–39.
Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (Eds, pp. 299–315. Routledge Taylor & Francis Group.
No supplementary information available.
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats

