A stage-specific affordance model of ChatGPT deployment in EFL writing

Criterion-level evidence from a quasi-experimental design

Authors

  • Zahra Zargaran University of Technology and Applied Sciences, Shinas, Oman https://orcid.org/0009-0004-9151-0387
  • Saif Said Bakhit Aldhahli University of Technology and Applied Sciences, Shinas, Oman

DOI:

https://doi.org/10.32674/ggjnen46

Keywords:

Affordance theory, , brainstorming, ChatGPT , EFL writing , written corrective feedback

Abstract

A stage-specific affordance model of AI-mediated writing instruction predicts that ChatGPT produces qualitatively different learning outcomes. However, this prediction has never been directly tested at the criterion level within a single controlled design. This study tests the model empirically using a quasi-experimental pre-test/post-test design. Eighty-four intermediate-level EFL undergraduates at a Gulf-region university were assigned to three intact classes; ChatGPT as a pre-writing brainstorming scaffold, ChatGPT as a post-drafting written corrective feedback tool, and a conventional instruction. Writing was assessed with an institutionally validated four-criterion rubric. One-way ANCOVA with Bonferroni correction revealed a theoretically coherent reversal; the brainstorming group significantly outperformed the feedback group on Task Response and Organisation, while the feedback group outperformed the brainstorming group on Grammatical Range/ Accuracy and Vocabulary. Both ChatGPT conditions outperformed the control on all criteria. These findings offer a  criterion-level validation of a stage-specific affordance model for sequencing ChatGPT in L2 writing.

 

 

Author Biographies

  • Zahra Zargaran, University of Technology and Applied Sciences, Shinas, Oman

    ZAHRA ZARGARAN, PhD, is an Assistant Professor of Applied Linguistics and ELT at the University of Technology and Applied Sciences, Shinas, Oman, with more than 19 years of university-level teaching experience across Iran and Oman. Her research spans teacher cognition, language assessment, AI-integrated pedagogy, and written feedback, with publications in international and Scopus-indexed journals. She is a certified trainer by the British Council, Cambridge University, and IDP Australia. Dr. Zargaran is committed to advancing language education through research, innovation, and professional development. Email: Zahra.zaragaran@utas.edu.om

  • Saif Said Bakhit Aldhahli, University of Technology and Applied Sciences, Shinas, Oman

    SAIF SAID BAKHIT ALDHAHLI, MA, is a lecturer at the University of Technology and Applied Sciences, Shinas, Oman, with an MA in Applied Linguistics from the University of Bedfordshire. His research resides at the intersection of second language acquisition (SLA) and curriculum design, with a particular focus on fostering learner autonomy through innovative teaching methods. He is dedicated to refining assessment and feedback frameworks to better support student growth, alongside his specialized interest in Phonetic Engineering and the systematic development of oral proficiency. Email: Saif.Aldhahli@utas.edu.om

References

Alharbi, M. A. (2023). Exploring the potential of ChatGPT as an EFL writing assistant: Benefits and challenges. English as a Foreign Language International Journal, 27(1), 1–18. https://doi.org/10.24205/03276716.2023.1

Al-Shabandar, R., Hussain, A., & Liatsis, P. (2022). Artificial intelligence in EFL writing: Attitudes and perceptions of Gulf-region learners. Computer Assisted Language Learning, 35(9), 2190–2215. https://doi.org/10.1080/09588221.2021.1970756

Bai, L., & Hu, G. (2022). AI-assisted brainstorming and its effects on EFL learners’ argumentative writing. System, 109, Article 102864. https://doi.org/10.1016/j.system.2022.102864

Bannister, P., Alcalde Peñalver, E., & Santamaría Urbieta, A. (2024). International students and generative artificial intelligence: A cross-cultural exploration of HE academic integrity policy. Journal of International Students, 14(3), 149–170. https://doi.org/10.32674/jis.v14i3.6277

Bitchener, J., & Knoch, U. (2010). Raising the linguistic accuracy level of advanced L2 writers with written corrective feedback. Journal of Second Language Writing, 19(4), 207–217. https://doi.org/10.1016/j.jslw.2010.10.002

Cavaleri, M., & Dianati, S. (2023). You want me to check your grammar again? The usefulness of ChatGPT as a grammar checker and language tutor for EFL learners. Journal of China Computer-Assisted Language Learning, 3(1), 137–154. https://doi.org/10.1515/jccall-2023-0009

Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, Article 100197. https://doi.org/10.1016/j.caeai.2023.100197

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

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

Ferris, D. R. (2004). The “grammar correction” debate in L2 writing: Where are we, and where do we go from here? Journal of Second Language Writing, 13(1), 49–62. https://doi.org/10.1016/j.jslw.2004.04.005

Flower, L., & Hayes, J. R. (1981). A cognitive process theory of writing. College Composition and Communication, 32(4), 365–387. https://doi.org/10.2307/356600

Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.

Guo, K., Wang, J., & Chu, S. K. W. (2023). Using ChatGPT to provide formative feedback on EFL writing: Comparing AI and teacher feedback. Language Learning & Technology, 27(2), 1–23. https://www.lltjournal.org/item/3676

Hong, S., & Shin, Y. K. (2025). Effects of three levels of AI integration on second language academic writing: Evaluating restricted, guided, and free use of ChatGPT. System, 134, Article 103820. https://doi.org/10.1016/j.system.2025.103820

Huang, X., Zou, D., Cheng, G., & Xie, H. (2024). A systematic review and meta-analysis of AI-assisted language learning: Moderating effects of tool type, learning focus, and study design. Computers & Education, 199, Article 104967. https://doi.org/10.1016/j.compedu.2023.104967

Hutchby, I. (2001). Technologies, texts and affordances. Sociology, 35(2), 441–456. https://doi.org/10.1177/S0038038501000219

Hyland, K. (2004). Genre and second language writing. University of Michigan Press.

Ittefaq, M., Zain, A., Arif, R., Ahmad, T., Khan, L., & Seo, H. (2025). Factors influencing international students’ adoption of generative artificial intelligence: The mediating role of perceived values and attitudes. Journal of International Students, 15(7), 127–156. https://doi.org/10.32674/fnwdpn48

Kang, E., & Han, Z. (2015). The efficacy of written corrective feedback in improving L2 written accuracy: A meta-analysis. Modern Language Journal, 99(1), 1–18. https://doi.org/10.1111/modl.12189

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for EFL/ESL writing: Affordances, limitations, and the way forward. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162336

Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410

Manchón, R. M. (Ed.). (2011). Learning-to-write and writing-to-learn in an additional language. John Benjamins.

Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5), Article e07014. https://doi.org/10.1016/j.heliyon.2021.e07014

Nelson, E. C. (2026). Structured AI workflows in Saudi EMI research writing: Performance gains, equity, and academic integrity across three semesters. Journal of International Students, 16(15), 81–102. https://doi.org/10.32674/e7jdhc67

OpenAI. (2022). Introducing ChatGPT. https://openai.com/blog/chatgpt

Victori, M. (1999). An analysis of writing knowledge in EFL composing: A case study of two effective and two less effective writers. System, 27(4), 537–555. https://doi.org/10.1016/S0346-251X(99)00049-4

Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies, 28(11), 13943–13967. https://doi.org/10.1007/s10639-023-11742-4

Zamel, V. (1982). Writing: The process of discovering meaning. TESOL Quarterly, 16(2), 195–209. https://doi.org/10.2307/3586792

Zhang, Y., & Liu, Y. (2026). Designing ChatGPT-mediated feedback activities in EFL writing: A design-based study of the dialogic feedback triangle. Assessment & Evaluation in Higher Education, 51(1), 137–160. https://doi.org/10.1080/02602938.2025.2571846

Zhang, Z. (V.), & Hyland, K. (2024). The role of digital literacy in student engagement with automated writing evaluation (AWE) feedback on second language writing. Computer Assisted Language Learning. Advance online publication. https://doi.org/10.1080/09588221.2023.2256815

Downloads

Published

2026-08-02

Issue

Vol. 16 No. 17 (2026): Journal of International Students

Section

Research Articles (English, regular edition)

Categories

License

Copyright (c) 2026 Zahra Zargaran, Saif Said Bakhit Aldhahli

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

https://creativecommons.org/licenses/by/4.0

How to Cite

Zargaran, Z., & Said Bakhit Aldhahli, S. . (2026). A stage-specific affordance model of ChatGPT deployment in EFL writing: Criterion-level evidence from a quasi-experimental design. Journal of International Students, 16(17), 339-358. https://doi.org/10.32674/ggjnen46