Peer e-Feedback and ChatGPT-4o in EFL Writing: A Cognitive-Interpersonal Comparison Based on EFL Students

Keywords: peer e-feedback; chatgpt-4o; academic writing; feedback comparison

Abstract

Background: Although both peer and AI-generated feedback are increasingly used in EFL writing instruction, little is known about how they differ in terms of cognitive depth and interpersonal delivery. Existing studies often overlook the mechanisms through which feedback operates, limiting instructors’ ability to design balanced feedback ecosystems.

Purpose: To provide a comparative analysis of peer and ChatGPT-4o feedback on undergraduate EFL academic writing, drawing on Hattie and Timperley’s (2007) cognitive model and Hyland and Hyland’s (2006) interpersonal feedback strategies. It aims to identify how each source targets task, process, and self-regulation levels, and how their rhetorical styles shape learner engagement.

Method: Thirty Iranian undergraduate EFL students participated in a qualitative classroom-based study in which each essay received both peer and ChatGPT-4o feedback. A total of 430 peer comments and 224 ChatGPT feedback units were coded deductively using validated analytical frameworks. Inter-rater reliability for 20% of the dataset yielded substantial agreement (κ = .82).

Results: Peer feedback was subjective, socially expressive, and frequently mitigated, with comments focusing on sentence-level issues but also including self-regulation prompts (15%) that encouraged reflection and decision-making. ChatGPT-4o provided predominantly task-level feedback (94%), characterized by structured, consistent, and objective guidance on grammar, organization, and coherence. However, its feedback exhibited minimal interpersonal variation and rarely promoted metacognitive engagement.

Conclusion: The findings indicate that peer and AI-generated feedback serve complementary pedagogical functions: AI offers technical accuracy and consistency, while peer feedback contributes emotional support and occasional reflective prompts. A hybrid feedback model that integrates both sources may therefore enhance the revision process in EFL writing instruction.

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Author Biographies

Hossein Bozorgian, University of Mazandaran, Babolsar, Mazandaran, Iran

Hossein Bozorgian is an Assistant Professor in University of Mazandaran and graduated Masters by Research and PhD in Applied Linguistics from Queensland University of Technology, Australia. He worked as a Research Assistant in both State and National projects on Education for 4 years in Australia in 2009-2012. He has been teaching undergraduate courses such as listening and speaking, Writing and research and postgraduate courses such as Principles of language teaching and Practicum courses. He has made dozens of publications in a few aspects of Applied Linguistics in top tier journals (Cambridge University Press, Francis & Tylor, Wiley, Springer and Elsevier) and listening comprehension, in particular. He has been an editorial board of some international journals, Director-in-charge of ISELT journal and Director of Persian Language Center in University of Mazandaran.

Hossein Rahimi, University of Mazandaran, Babolsar, Mazandaran, Iran

Hossein Rahimi is a Ph.D. candidate in Applied Linguistics at the University of Mazandaran, where his doctoral research focuses on the intersection of language pedagogy, learner identity, and the integration of AI in education. He holds an M.A. in English Language Teaching from Ferdowsi University of Mashhad, one of Iran’s most prestigious universities. Rahimi has extensive experience teaching English at various language institutes and currently teaches at the university level. He has also served as an assistant researcher on numerous academic projects, collaborating with university professors in the fields of language education, applied linguistics, and educational technology.

References

Alnemrat, A., Aldamen, H., Almashour, M., Al-Deaibes, M., & AlSharefeen, R. (2025). AI vs. teacher feedback on EFL argumentative writing: a quantitative study. Frontiers in Education, 10, 1614673. https://doi.org/10.3389/feduc.2025.1614673

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

Banihashem, S.K., Kerman, N.T., Noroozi, O., Moon, J. & Drachsler, H. (2024). Feedback sources in essay writing: peer-generated or AI-generated feedback? International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00455-4

Barrot, J. S. (2023). Using ChatGPT for second language writing: Pitfalls and potentials. Assessing Writing, 57(1), 100745. https://doi.org/10.1016/j.asw.2023.100745

Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. https://doi.org/10.3102/00346543065003245

Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354

Chan, C., Chen, J., Wang, W., Jiang, Y., Fang, T., Liu, X., & Song, Y. (2023). ChatGPT evaluation on sentence-level relations: A focus on temporal, causal, and discourse relations (arXiv preprint No. 2304.14827). https://doi.org/10.48550/arXiv.2304.14827

Cheung, K. K. C., & Tai, K. W. H. (2021). The use of intercoder reliability in qualitative interview data analysis in science education. Research in Science & Technological Education, 41(3), 1–21. https://doi.org/10.1080/02635143.2021.1993179

Chowdhury, M. F. (2015). Coding, sorting and sifting of qualitative data analysis: Debates and discussion. Quality & Quantity, 49(3), 1135–1143. https://doi.org/10.1007/s11135-014-0039-2

Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., & Sun, M. (2023). UltraFeedback: Boosting language models with high-quality feedback (arXiv preprint No. 2310.01377). https://doi.org/10.48550/arXiv.2310.01377

Cui, Y., & Schunn, C. D. (2024). Peer feedback that consistently supports learning to write and read: providing comments on meaning-level issues. Assessment & Evaluation in Higher Education, 49(8), 1168–1181. https://doi.org/10.1080/02602938.2024.2364025

Dempere, J. M., Modugu, K. P., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, 1206936. https://doi.org/10.3389/feduc.2023.1206936

Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00425-2

Fang, T., Yang, S., Lan, K., Wong, D. F., Hu, J., Chao, L. S., & Zhang, Y. (2023). Is ChatGPT a highly fluent grammatical error correction system? A comprehensive evaluation (arXiv preprint No. 2304.01746). https://arxiv.org/abs/2304.01746

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 1–15. https://doi.org/10.1080/14703297.2023.2195846

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. https://doi.org/10.3389/frai.2023.1162454

Gale, N. K., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013). Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Medical Research Methodology, 13, 117. https://doi.org/10.1186/1471-2288-13-117

Goldsmith, L. J. (2021). Using framework analysis in applied qualitative research. The Qualitative Report, 26(6), 2061–2076. https://doi.org/10.46743/2160-3715/2021.5011

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Hofstede, G. (2001). Culture’s Recent Consequences: Using Dimension Scores in Theory and Research. International Journal of Cross Cultural Management, 1(1), 11–17. https://doi.org/10.1177/147059580111002

Huang, J., & Teng, M. F. (2025). Peer feedback and ChatGPT-generated feedback on Japanese EFL students’ engagement in a foreign language writing context. Digital Applied Linguistics, 2, 102469. https://doi.org/10.29140/dal.v2.102469

Huang, Y., Wang, J., & Chen, X. (2021). Automated writing evaluation in EFL contexts: Effects on writing quality and revision behavior. Computer Assisted Language Learning, 34(3), 234–257. https://doi.org/10.1080/09588221.2020.1786712

Hyland, F. (2010). Future directions in feedback on second language writing: Overview and research agenda. International Journal of English Studies, 10(2), 171–182. https://doi.org/10.6018/ijes/2010/2/119251

Hyland, K., & Hyland, F. (2006). Feedback on second language students’ writing. Language Teaching, 39(2), 83–101. https://doi.org/10.1017/s0261444806003399

Jiang, F. & Hyland, K. (2025). Rhetorical distinctions: Comparing metadiscourse in essays by ChatGPT and students. English for Specific Purposes, 79, 17–29. https://doi.org/10.1016/j.esp.2025.03.001

Johnson, C. I., Priest-Walker, H. A., Durlach, P. J., & Serge, S. R. (2012). The effect of feedback specificity in a virtual training environment. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 1576–1580. https://doi.org/10.1177/1071181312561314

Kayaalp, F., Meral, E., & Başcı Namlı, Z. (2022). An analysis of the effect of writing-to-learn activities regarding students’ academic achievement and self-regulation skills in writing. Participatory Educational Research, 9(1), 324-348. https://doi.org/10.17275/per.22.18.9.1

Kerman, N. T., Noroozi, O., Banihashem, S. K., Karami, M., & Biemans, H. J. A. (2022). Online peer feedback patterns of success and failure in argumentative essay writing. Interactive Learning Environments, 32(2), 1–13. https://doi.org/10.1080/10494820.2022.2093914

Khodadust, M. R. (2024). Politeness and corrective feedback: Immediate and delayed performance. Iranian Journal of Language Teaching Research, 12(2), 137–155. https://doi.org/10.30466/ijltr.2024.54354.2395

Kipp, M. (2024). From GPT-3.5 to GPT-4.o: A leap in AI’s medical exam performance. Information, 15(9), 543. https://doi.org/10.3390/info15090543

Kopec, A. (2023). Policy feedback & research methods: how qualitative research designs with marginalized groups inform theory. International Journal of Qualitative Methods, 22(10). https://doi.org/10.1177/16094069231217915

Li, C., Yang, Z., & Yang, Y. (2024). The impact of peer feedback on student learning effectiveness: A meta-analysis based on 39 experimental or quasi-experimental studies. In J. Gan, Y. Pan, J. Zhou, D. Liu, X. Song, & Z. Lu (Eds.), Computer science and educational informatization: CSEI 2023 (Communications in Computer and Information Science, Vol. 1899, pp. 42–52). Springer. https://doi.org/10.1007/978-981-99-9499-1_4

Lipsch-Wijnen, I., & Dirkx, K. (2022). A case study of the use of the Hattie and Timperley feedback model on written feedback in thesis examination in higher education. Cogent Education, 9(1). https://doi.org/10.1080/2331186x.2022.2082089

Mandouit, L., & Hattie, J. (2023). Revisiting “The Power of Feedback” from the perspective of the learner. Learning and Instruction, 84(84). https://doi.org/10.1016/j.learninstruc.2022.101718

McHugh, M. L. (2012). Interrater reliability: The Kappa Statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/bm.2012.031

Mo, Z., & Crosthwaite, P. (2025). Exploring the affordances of generative AI large language models for stance and engagement in academic writing. Journal of English for Academic Purposes, 75, 101499. https://doi.org/10.1016/j.jeap.2025.101499

Nakamura, S. (2018). Insights from studies on written corrective feedback: Implications for language pedagogy. REFLections, 22, 89–102. https://doi.org/10.61508/refl.v22i0.112330

Noroozi, O., Alqassab, M., Kerman, N. T., Banihashem, S. K., & Panadero, E. (2024). Does perception mean learning? Insights from an online peer feedback setting. Assessment & Evaluation in Higher Education, 50(1), 1–15. https://doi.org/10.1080/02602938.2024.2345669

Shi, H., & Aryadoust, V. (2024). A systematic review of AI-based automated written feedback research. ReCALL, 36(2), 1–23. https://doi.org/10.1017/s0958344023000265

Stalmeijer, R. E., Klingberg, S., & Varpio, L. (2024). Using framework analysis methods for qualitative research: AMEE Guide No. 164. Medical Teacher, 46(5), 603–610. https://doi.org/10.1080/0142159X.2023.2259073

Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. B. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction, 91, 101894–101894. https://doi.org/10.1016/j.learninstruc.2024.101894

Tang, T., Sha, J., Zhao, Y., Wang, S., Wang, Z., & Shen, S. (2024). Unveiling the efficacy of ChatGPT in evaluating critical thinking skills through peer feedback analysis: Leveraging existing classification criteria. Thinking Skills and Creativity, 53, 101607–101607. https://doi.org/10.1016/j.tsc.2024.101607

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

Usher, M. (2025). Generative AI vs. instructor vs. peer assessments: a comparison of grading and feedback in higher education. Assessment & Evaluation in Higher Education, 50(6), 912-927. https://doi.org/10.1080/02602938.2025.2487495

Wang, J., Liang, Y., Meng, F., Sun, Z., Shi, H., Li, Z., Xu, J., Qu, J., & Zhou, J. (2023). Is ChatGPT a good NLG evaluator? A preliminary study. In Y. Dong, W. Xiao, L. Wang, F. Liu, & G. Carenini (Eds.), Proceedings of the 4th New Frontiers in Summarization Workshop (pp. 1–11). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.newsum-1.1

Wu, Y., & Schunn, C. D. (2021). The effects of providing and receiving peer feedback on writing performance and learning of secondary school students. American Educational Research Journal, 58(3), 492–526. https://doi.org/10.3102/0002831220945266

Xie, X., Zhang, L. J., & Wilson, A. J. (2025). Comparing ChatGPT Feedback and Peer Feedback in Shaping Students’ Evaluative Judgement of Statistical Analysis: A Case Study. Behavioral Sciences, 15(7), 884. https://doi.org/10.3390/bs15070884

Yoon, S.-Y., Miszoglad, E., & Pierce, L. R. (2023). Evaluation of ChatGPT feedback on ELL writers’ coherence and cohesion (arXiv Preprint No. 2310.06505). arXiv. https://doi.org/10.48550/arXiv.2310.06505

Yu, H., & Xie, Q. (2025). Generative AI vs. teachers: Feedback quality, feedback Uptake, and revision. Language Teaching Research Quarterly, 47, 113–137. https://doi.org/10.32038/ltrq.2025.47.07

Yu, S., & Hu, G. (2017). Understanding university students’ peer feedback practices in EFL writing: Insights from a case study. Assessing Writing, 33, 25–35. https://doi.org/10.1016/j.asw.2017.03.004

Yu, S., Lee, I., & Mak, P. (2015). Revisiting Chinese cultural issues in peer feedback in EFL writing: Insights from a multiple case study. The Asia-Pacific Education Researcher, 25(2), 295–304. https://doi.org/10.1007/s40299-015-0262-1

Zeevy-Solovey, O. (2024). Comparing peer, ChatGPT, and teacher corrective feedback in EFL writing: Students’ perceptions and preferences. Technology in Language Teaching & Learning, 6(3), 1482. https://doi.org/10.29140/tltl.v6n3.1482

Zemach, D. E., & Rumisek, L. A. (2005). Academic writing from paragraph to essay. MacMillan.

Zhan, Y., & Yan, Z. (2025). Students’ engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence. Assessment & Evaluation in Higher Education, 1–14. https://doi.org/10.1080/02602938.2025.2471821

Zhang, Z., & Hyland, K. (2022). Fostering student engagement with feedback: An integrated approach. Assessing Writing, 51, 100586. https://doi.org/10.1016/j.asw.2021.100586

Zou, S., Guo, K., Wang, J., & Liu, Y. (2025). Investigating students’ uptake of teacher- and ChatGPT-generated feedback in EFL writing: a comparison study. Computer Assisted Language Learning, 1–30. https://doi.org/10.1080/09588221.2024.2447279

Published
2025-12-30
How to Cite
BozorgianH., & RahimiH. (2025). Peer e-Feedback and ChatGPT-4o in EFL Writing: A Cognitive-Interpersonal Comparison Based on EFL Students. Journal of Language and Education, 11(4), 54-65. https://doi.org/10.17323/jle.2025.27195