TEACHER, AI, AND HYBRID FEEDBACK IN EFL WRITING: AN INTEGRATED THEORETICAL MODEL OF LEARNER PREFERENCES

Authors

DOI:

https://doi.org/10.46991/AFA/2026.22.1.110

Keywords:

EFL writing feedback, AI in education, Hybrid Feedback Models, Feedback Literacy, Technology Acceptance Model (TAM)

Abstract

This research examines the preferences of EFL university students in Algeria, regarding teacher, AI-generated, and hybrid feedback in academic writing. Based on a unified theoretical framework that integrates Sociocultural Theory (SCT), Feedback Literacy, and the Technology Acceptance Model (TAM), this study examines the influence of cognitive, affective, and technological factors on learners’ engagement with various feedback sources. Data were gathered from 226 EFL students using a validated self-report questionnaire and subsequently analyzed by exploratory factor analysis (EFA) and structural equation modeling (SEM). The results show that teacher feedback is the best predictor of how students like to get feedback overall. AI-generated feedback, on the other hand, is more of a supplement: students acknowledge its usefulness for grammatical accuracy and iterative revision, but its perceived usefulness alone does not significantly predict their preferences. Instead, behavioral engagement with AI tools proves to be a significant determinant, suggesting that interaction with AI, rather than cognitive evaluation, drives its acceptance. Mediation analysis further demonstrates that high-quality teacher feedback indirectly enhances feedback preference by fostering greater engagement with AI tools, supporting a synergistic relationship between human and automated feedback. The study contributes to the growing body of literature by offering an integrative, SEM-based comparison of teacher, AI, and hybrid feedback in an underexplored Algerian EFL context. The results underscore the pedagogical value of hybrid feedback models, where AI serves as a supportive tool rather than a replacement for teacher input. Implications for designing balanced, technology-enhanced writing instruction that integrates both human expertise and AI affordances are drawn.

Downloads

Download data is not yet available.

References

Aissi, R., & Mouas, S. (2024). Analyzing teachers’ perceptions of the impact of Moodle personalized positive feedback on foreign language students’ motivation and engagement. XLinguae, 17(1), 123–141. https://doi.org/10.18355/xl.2024.17.01.09

Alghannam, M. S. M. (2024). Artificial intelligence as a provider of feedback on EFL student compositions. World Journal of English Language, 15(2), 161. https://doi.org/10.5430/wjel.v15n2p161

Altamimi, D. H. F. (2025). Unlocking potential: Saudi EFL male students’ perspectives on AI tools for enhancing English writing proficiency. Arab World English Journal, 1, 40–58. https://doi.org/10.24093/awej/ai.3

Alyami, A., Alotaibi, S., & Khan, W. (2025). Saudi EFL learners’ perceptions of using artificial intelligence and its impact on their writing skills. Arab World English Journal, 16(1), 349–365. https://doi.org/10.24093/awej/vol16no1.22

Apridayani, A., Hongboontri, C., & Watanapokakul, S. (2026). The interplay of teacher cognition and student voice in feedback practices: A case study from Thai higher education. Social Sciences & Humanities Open, 13, 102521. https://doi.org/10.1016/j.ssaho.2026.102521

Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65(5), 676–696. https://doi.org/10.1177/0013164404272507

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

Enders, C. K. (2010). Applied missing data analysis. Guilford.

George, D., & Mallery, P. (1999). SPSS for Windows step by step: A simple guide and reference. Routledge.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling (R package version 0.5-6). https://CRAN.R-project.org/package=semTools

Kailin, Z., & Saeed, M. A. (2026). Chinese EFL learners’ engagement with ChatGPT feedback on academic writing: A case study in Malaysia. Computers and Composition, 79, 102976. https://doi.org/10.1016/j.compcom.2025.102976

Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/bf02291575

Li, L., Zhang, X., Zou, B., & Yang, Q. (2025). AI partner or peer partner? Exploring AI-mediated interaction in EFL pronunciation from a socio-cultural perspective. Learning, Culture and Social Interaction, 55, 100958. https://doi.org/10.1016/j.lcsi.2025.100958

Mansoor, H. S., Sumardjoko, B., & Sutopo, A. (2026). External variables influencing the attitudes of students toward AI acceptance in improving English writing: A systematic review. Frontiers in Artificial Intelligence, 8, 1719955. https://doi.org/10.3389/frai.2025.1719955

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

R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3.2). R Foundation for Statistical Computing.

Rahmi, R., Amalina, Z., Andriansyah, A., & Rodgers, A. (2024). Does it really help? Exploring the impact of AI-generated writing assistant on the students’ English writing. Studies in English Language and Education, 11(2), 998–1012. https://doi.org/10.24815/siele.v11i2.35875

Revelle, W. (2023). psych: Procedures for psychological, psychometric, and personality research (R package version 2.3.9). Northwestern University.

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120. https://doi.org/10.1007/s11336-008-9101-0

Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Lawrence Erlbaum.

Storch, N. (2018). Written corrective feedback from sociocultural theoretical perspectives: A research agenda. Language Teaching, 51(2), 262–277. https://doi.org/10.1017/s0261444818000034

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, 100270. https://doi.org/10.1016/j.caeai.2024.100270

Tsao, J.-J. (2025). EFL students’ perceptions of reading portfolios and teacher feedback on reflective writing. Arab World English Journal, 16(1). https://doi.org/10.24093/awej/vol16no1.1

Wang, Z., & Han, F. (2022). The effects of teacher feedback and automated feedback on cognitive and psychological aspects of foreign language writing: A mixed-methods research. Frontiers in Psychology, 13, 909802. https://doi.org/10.3389/fpsyg.2022.909802

Weng, F., Zhao, C. G., & Chen, S. (2024). Effects of peer feedback in English writing classes on EFL students’ writing feedback literacy. Assessing Writing, 61, 100874. https://doi.org/10.1016/j.asw.2024.100874

West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Sage.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer.

Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A grammar of data manipulation (R package version 1.1.3).

Yang, L. F., Liu, Y., & Xu, Z. (2022). Examining the effects of self-regulated learning-based teacher feedback on English-as-a-foreign-language learners’ self-regulated writing strategies and writing performance. Frontiers in Psychology, 13, 1027266. https://doi.org/10.3389/fpsyg.2022.1027266

Zeevy Solovey, O. (2024). Comparing peer, ChatGPT, and teacher corrective feedback in EFL writing: Students’ perceptions and preferences. Technology in Language Teaching and Learning. https://doi.org/10.29140/tltl.v6n3.1482

Zhang, Z., Aubrey, S., Huang, X., & Chiu, T. K. F. (2025). The role of generative AI and hybrid feedback in improving L2 writing skills: A comparative study. Innovation in Language Learning and Teaching, 1–19. https://doi.org/10.1080/17501229.2025.2503890

Downloads

Published

2026-06-09

Issue

Section

Methodology

How to Cite

Mouas, S. (2026). TEACHER, AI, AND HYBRID FEEDBACK IN EFL WRITING: AN INTEGRATED THEORETICAL MODEL OF LEARNER PREFERENCES. Armenian Folia Anglistika, 22(1(33), 110-138. https://doi.org/10.46991/AFA/2026.22.1.110