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Arab World English Journal (AWEJ) Special Issue on Artificial Intelligence, No. 3. 2026. Pp.102-117
Faculty of Education, Universiti Kebangsaan Malaysia, Selangor, Malaysia
Faculty of Education, Universiti Kebangsaan Malaysia, Selangor, Malaysia
Faculty of Education, Universiti Kebangsaan Malaysia, Selangor, Malaysia
AI writing tools are becoming more crucial in helping university students with their English writing as generative AI in education continues to grow. This study explores how essential system features affect students’ writing performance through technology acceptance routes. And it also researches the moderating function of self-regulated learning. This study used a questionnaire survey to gather data from 531 English major students using a quantitative design. We used partial least squares structural equation modeling to examine the links between system features, perceived usefulness, perceived ease of use, attitudes, behavioral intentions, and writing performance. The findings demonstrate that perceived utility and perceived ease of use, which in turn affect attitudes and behavioral intentions, are highly predicted by AI system attributes. Behavioral intention positively affects writing performance, and self-regulated learning strengthens this relationship. This study contributes to understanding the mechanisms through which AI tools enhance writing development and provide practical implications for improving AI tool design and integrating them into writing instruction. Future research should further examine long-term effects using longitudinal or process-based data.
Wu, J., Husnin, H., & Mohamad, M. (2026). Modeling the Impact of AI Features on Writing Performance: A TAM–SRL Integrated Framework. Arab World English Journal, Special Issue on Artificial Intelligence, (3):102-117
Aiken, L. S., & West, S. G. (2010). Multiple regression: Testing and interpreting interactions. SAGE Publications, Inc.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences, rev. Ed (pp. xv, 474). Lawrence Erlbaum Associates, Inc.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
Doleck, T., Bazelais, P., & Lemay, D. J. (2017). Examining the antecedents of social networking sites use among CEGEP students. Education and Information Technologies, 22(5), 2103–2123. https://doi.org/10.1007/s10639-016-9535-4
Fleckenstein, J., Liebenow, L. W., & Meyer, J. (2023). Automated feedback and writing: A multi-level meta-analysis of effects on students’ performance. Frontiers in Artificial Intelligence, 6, 1162454. https://doi.org/10.3389/frai.2023.1162454
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Second edition). SAGE.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair Jr, J., Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2023). Advanced issues in partial least squares structural equation modeling. saGe publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited. https://www.emerald.com/insight/content/doi/10.1108/s1474-7979/(2009/)0000020014/full/html
Kim, J., Yu, S., Detrick, R., & Li, N. (2025). Exploring students’ perspectives on generative AI-assisted academic writing. Education and Information Technologies, 30(1), 1265–1300. https://doi.org/10.1007/s10639-024-12878-7
Mekheimer, M. (2025). Generative AI-assisted feedback and EFL writing: A study on proficiency, revision frequency and writing quality. Discover Education, 4(1), 170. https://doi.org/10.1007/s44217-025-00602-7
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), 7. https://doi.org/10.1186/s40468-025-00343-2
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422.https://doi.org/10.3389/fpsyg.2017.00422
Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
Venkatesh, Morris, Davis, & Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Wilson, J., Ahrendt, C., Fudge, E. A., Raiche, A., Beard, G., & MacArthur, C. (2021). Elementary teachers’ perceptions of automated feedback and automated scoring: Transforming the teaching and learning of writing using automated writing evaluation. Computers & Education, 168, 104208. https://doi.org/10.1016/j.compedu.2021.104208
Yang, H., Gao, C., & Shen, H. (2024). Learner interaction with, and response to, AI-programmed automated writing evaluation feedback in EFL writing: An exploratory study. Education and Information Technologies, 29(4), 3837–3858. https://doi.org/10.1007/s10639-023-11991-3
Yang, L. F., & Zhang, L. J. (2023). Self-regulation and student engagement with feedback: The case of Chinese EFL student writers. Journal of English for Academic Purposes, 63, 101226.https://doi.org/10.1016/j.jeap.2023.101226
Zhai, N., & Ma, X. (2023). The effectiveness of automated writing evaluation on writing quality: A meta-analysis. Journal of Educational Computing Research, 61(4), 875–900. https://doi.org/10.1177/07356331221127300
Zhao, X., Lynch Jr., J. G., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257
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, 1619391.https://doi.org/10.3389/fpsyg.2025.1619391
Jiaxia Wu is pursuing her PhD in learning technology and innovation at the Faculty of Education, Universiti Kebangsaan Malaysia (UKM). Her research interests include the application of technology in English language teaching and English teachers’ digital competence improvement.ORCID: https://orcid.org/0009-0000-8820-0389
Dr. Hazrati Husnin is a a senior lecturer at the Faculty of Education, Universiti Kebangsaan Malaysia (UKM). Her main research interests is information resources and technology. ORCID: https://orcid.org/0000-0002-6165-2411
Dr. Maslawati Mohamad is a senior lecturer at the Faculty of Education, Universiti Kebangsaan Malaysia (UKM). Her main research interests are innovations in teaching and learning in ESL context, teaching reading in ESL context and English for Specific Purposes. ORCID: https://orcid.org/0000-0002-8979-5895