The growing demand for English skills has led to large classes and teachers unable to give feedback to everyone. Large language models (LLMs) such as ChatGPT offer a solution to this issue by giving students more access to feedback. However, there is limited data showing the ability of this technology to adequately meet the needs of second language (L2) learners seeking to develop their writing skills. Furthermore, few studies have been conducted that compared feedback from teachers and LLMs. To address that gap, this study compared the feedback from ChatGPT with that of a human teacher in terms of its perceived usefulness and effect on the academic writing of low-intermediate L2 university students (N = 69). Within a quasi-experimental design, Group A received ChatGPT feedback and Group B received teacher feedback. Each group completed three writing tasks in six weeks. The data were gathered using blind scoring of drafts, success on revision, and semi-structured interviews. Using a mixed methods approach, the results showed that the students exposed to teacher feedback had greater comprehension, revised more successfully, and reported higher trust in the feedback compared to those who received feedback from ChatGPT. Although ChatGPT appeared useful in fixing surface-level concerns, such as spelling and grammar, it failed to tackle deeper issues and provide individualized, context-specific feedback essential to promote student engagement and self-regulation. Nevertheless, LLMs could complement teacher feedback, particularly where resources are limited. Thus, a hybrid model incorporating both types of feedback might be more effective supporting L2 writing development.

Authors:
Khaled Aldossary, King Faisal University, Saudi Arabia

About the Presenter(s)
Khaled Aldossary is currently an Associate Professor of Applied Linguistics and Head of the Department of English Language, King Faisal University, Al Ahsa, Saudi Arabia.

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