Comparative Analysis of AI and Human Instructors in Providing Written Corrective Feedback: A Case Study on ChatGPT
Authors
Academy of Language Studies, Universiti Teknologi MARA, Johor Branch, Pasir Gudang Campus, Masai (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.100300016
Subject Category: Social science
Volume/Issue: 10/3 | Page No: 214-222
Publication Timeline
Submitted: 2026-02-28
Accepted: 2026-03-05
Published: 2026-03-24
Abstract
Corrective feedback is one of the instructional methods utilised in learning writing. Any indication that a student’s expression might contain an error of some sort is referred to as corrective feedback. Obtaining feedback is a component of the process of writing improvement. Traditionally, this feedback has been provided by human instructors, who meticulously review students' work to pinpoint areas of improvement. However, with the advent of AI technology, particularly exemplified by the ChatGPT model, the landscape of feedback provision is undergoing a significant transformation. Thus, this study aims to investigate the differences between AI and human instructors in providing written corrective feedback, focusing on the ChatGPT AI model within the context of ESL writing instruction in a higher learning institution in Malaysia. The study addresses a gap in research by examining the types of corrective feedback offered by ESL instructors and ChatGPT, aiming to uncover potential differences between them. The research objectives include identifying the types of corrective feedback provided by ESL instructors and ChatGPT and exploring any disparities between them. The study's significance lies in its contribution to understanding written corrective feedback in ESL writing, offering insights for researchers, educators, learners, and policymakers. This research seeks to inform pedagogical practices and educational policies by bridging traditional ESL instruction with emerging AI technologies. Overall, this study aims to shed light on the potential of AI-driven tools like ChatGPT to enhance ESL writing instruction while recognising the enduring value of human expertise in language teaching.
Keywords
AI corrective feedback, ChatGPT corrective feedback, ESL writing instruction
Downloads
References
1. (2023). Global Views on A.I. 2023. Ipsos. Retrieved from https://www.ipsos.com/sites/default/files/ct/news/documents/2023-07/Ipsos%20Global%20AI%202023%20Report-WEB.pdf [Google Scholar] [Crossref]
2. Al-Rubai’ey, F., & Nassaji, H. (2013). Direct and Indirect Metalinguistic Feedback: In Issues in TEFL in the Arab World (pp. 203-231). Sultan Qaboos University Press. Retrieved from https://www.researchgate.net/publication/267865580_Direct_and_indirect_metalinguistic_feedback_A_matter_of_suitability_rather_than_superiority?enrichId=rgreq-2a64e237fa6d2978623d4f30733266b2-XXX&enrichSource=Y292ZXJQYWdlOzI2Nzg2NTU4MDtBUzoxMDM1NjMzMTI4NjM [Google Scholar] [Crossref]
3. Berkant, H. G., Derer, N. B., & Derer, O. K. (2020). The effects of different types of written corrective feedback on students’ texting mistakes. English Language Teaching Educational Journal, 3(3), 174. https://doi.org/10.12928/eltej.v3i3.3136 [Google Scholar] [Crossref]
4. Bitchener J., Young S., Cameron D. (2005). The effect of different types of corrective feedback on ESL student writing. Journal of Second Language Writing, 14(3), 191–205. https://doi.org/10.1016/j.jslw.2005.08.001 [Google Scholar] [Crossref]
5. Chandler J. (2003). The efficacy of various kinds of error feedback for improvement in the accuracy and fluency of L2 student writing. Journal of Second Language Writing, 12(3), 267–296. https://doi.org/10.1016/s1060-3743(03)00038-9 [Google Scholar] [Crossref]
6. Derakshan, A., Salehi, D., & Rahimzadeh, M. (2015). Computer-Assisted Language Learning (Call): Pedagogical Pros and Cons (Vol. 4). International Journal of English Language and Literature Studies. https://doi.org/10.18488/journal.23/2015.4.3/23.3.111.120 [Google Scholar] [Crossref]
7. Ellis, R. (2008). A typology of written corrective feedback types. ELT Journal, 63(2), 97–107.doi: https://doi.org/10.1093/elt/ccn023 [Google Scholar] [Crossref]
8. Gans, M. (2023, May 2). What Is ChatGPT? Featuring ChatGPT. Byte-Sized Insights. https://medium.com/byte-sized-insights/what-is-chatgpt-featuring-chatgpt-68f52718ffdc [Google Scholar] [Crossref]
9. Glaser, B., & Strauss, A. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Mill Valley, CA: Sociology Press. [Google Scholar] [Crossref]
10. Han, J., Yoo, H., Kim, Y., Myung, J., Kim, M., Lim, H., Kim, J., Lee, T., Hong,H., Ahn, S., & Oh, A. (2023). RECIPE: How to integrate ChatGPT into EFL writing education. arXiv preprint. https://doi.org/10.1145/3573051.3596200 [Google Scholar] [Crossref]
11. Hao, Y., Lee, K., Chen, S.-T., & Sim, S. (2019). An evaluative study of a mobile application for middle school students struggling with English vocabulary learning. Computers in Human Behavior, 95, 208-216. https://doi.org/10.1016/j.chb.2018.10.013 [Google Scholar] [Crossref]
12. Kessler, G., & Bikowski, D. (2010). Developing collaborative autonomous learning abilities in computer-mediated language learning: attention to meaning among students in wikispace. Computer Assisted Language Learning, 23(1), 41–58. https://doi.org/10.1080/09588220903467335 [Google Scholar] [Crossref]
13. Kessler M. (2023). Supplementing mobile-assisted language learning with reflective journal writing: a case study of Duolingo users’ metacognitive awareness. Comput. Assist. Lang. Learn. 36, 1040–1063. https://doi.org/10.1080/09588221.2021.1968914 [Google Scholar] [Crossref]
14. Kim, S., Shim, J., & Shim, J. (2023). A Study on the Utilization of OpenAI ChatGPT as a Second Language Learning Tool. Journal of Multimedia Information System, 10(1), 79–88. https://doi.org/10.33851/JMIS.2023.10.1.79 [Google Scholar] [Crossref]
15. Lee, J. W., & Yoon, K. O. (2020). Effects of written corrective feedback on using the english indefinite article in EFL learners’ writing. English Teaching(South Korea), 75(2). https://doi.org/10.15858/engtea.75.2.202006.21 [Google Scholar] [Crossref]
16. Mikheeva, N. F., & Petrova, M. G. (2021). Artificial Intelligence in Academic Writing Teaching. 4th Sintok International Conference on Social Science and Management, 37–47. https://doi.org/10.17632/4mygjn3j4g.1 [Google Scholar] [Crossref]
17. Liu, M., Z. Moore, L. Graham and S. Lee, 2002. A look at the research on computer-based technology use in second language: A review of literature from 1990-2000. The Journal of Research on Technology in Education, 34(3): 1-54. https://doi.org/10.1080/15391523.2002.10782348 [Google Scholar] [Crossref]
18. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x [Google Scholar] [Crossref]
19. Mohamed, A. M. (2023). Exploring the potential of an AI-based Chatbot (ChatGPT) in enhancing English as a Foreign Language (EFL) teaching: perceptions of EFL Faculty Members. Education and Information Technologies, 1-23. https://doi.org/10.1007/s10639-023-11917-z [Google Scholar] [Crossref]
20. Miao, J., Chang, J., & Ma, L. (2023). Research Trends of Written Corrective Feedback in L2 Writing: A Bibliometric Analysis. SAGE Open, 13(1). https://doi.org/10.1177/21582440221135172 [Google Scholar] [Crossref]
21. Nassaji, Hossein & Swain, Merrill. (2000). A Vygotskian Perspective on Corrective Feedback in L2: The Effect of Random Versus Negotiated Help on the Learning of English Articles. Language Awareness - LANG AWARE. 9. 34-51. https://doi.org/10.1080/09658410008667135 [Google Scholar] [Crossref]
22. Nguyen, T. T. H. (2023). EFL teachers’ perspectives toward the use ofChatGPT in writing classes: A case study at Van Lang University. International Journal of Language Instruction, 2(3), 1-47. https://doi.org/10.54855/ijli.23231 [Google Scholar] [Crossref]
23. Ortega, L. (2009). Interaction and attention to form in L2 text-based computer-mediated communication. In Mackey, A. & Polio, C. (eds.), Multiple perspectives on interaction (pp. 226–253). New York: Routledge. [Google Scholar] [Crossref]
24. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press [Google Scholar] [Crossref]
25. Schmidt-Fajlik, R. (2023). ChatGPT as a Grammar Checker for Japanese English Language Learners: A Comparison with Grammarly and ProWritingAid. AsiaCALL Online Journal, 14(1), 105-119. https://doi.org/10.54855/acoj.231417 [Google Scholar] [Crossref]
26. Ohta, A. S. (2001). Second language acquisition processes in the classroom: Learning Japanese. Routledge. https://doi.org/10.4324/9781410604712 [Google Scholar] [Crossref]
27. Schmidt, R. (1990). The role of consciousness in second language learning. Applied Linguistics, 11, 129-58. [Google Scholar] [Crossref]
28. Sanosi, A. (2022). TO Err is Human: Comparing Human and Automated Corrective Feedback. Information Technologies and Learning Tools, 90, 149-161. https://doi.org/10.33407/itlt.v90i4.4980 [Google Scholar] [Crossref]
29. Schmidt, R. (1994). Implicit learning and the cognitive unconscious: Of artificial grammars and SLA. In N. Ellis (Ed.), Implicit and explicit learning of languages (pp. 165-209). London: Academic Press. [Google Scholar] [Crossref]
30. Syting, C., Malisobo, J., Salce, M., & Roasol, M. (2023). Teachers’ Written Corrective Feedback Strategies through the Lens of the Students. Journal Corner of Education, Linguistics, and Literature, 3(2), 171-186. Retrieved from https://doi.org/10.54012/jcell.v3i2.227 [Google Scholar] [Crossref]
31. Van Beuningen, C., de Jong, N. H., & Kuiken, F. (2008). The Effect of Direct and Indirect Corrective Feedback on L2 Learners’ Written Accuracy. ITL - International Journal of Applied Linguistics, 156, 279–296. https://doi.org/10.2143/itl.156.0.2034439 [Google Scholar] [Crossref]
32. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press [Google Scholar] [Crossref]
33. Wei, W. (2020). Written Corrective Feedback Strategies Employed by University English Lecturers: A Teacher Cognition Perspective. SAGE Open. https://doi.org/10.1177/2158244020934886 [Google Scholar] [Crossref]
34. Yutong, Z., Leqi, Z., Rongxiao, Z., & Hang, S. (2024). Exploring AI-Generated Feedback on English Writing: A Case Study of ChatGPT. US-China Foreign Language, 22(3), 144-153. doi: https://doi.org/10.17265/1539-8080/2024.03.002 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Impact of Ownership Structure on Dividend Payout Policy of Listed Plantation Companies in Sri Lanka
- Urban Sustainability in North-East India: A Study through the lens of NER-SDG index
- Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics
- The Fractured Self in Julian Barnes' Postmodern Fiction: Identity Crisis and Deflation in Metroland and the Sense of an Ending
- Impact of Flood on the Employment, Labour Productivity and Migration of Agricultural Labour in North Bihar