Examining the Efficacy of ChatGPT and Human-Derived Corrective Feedback in Addressing Grammatical Errors in Saudi EFL Students' Compositions
Bibliographic record
Abstract
Employing an explanatory mixed-methods approach, this study compared the affordances of ChatGPT’s written corrective feedback (WCF) and human corrective feedback in fostering linguistic accuracy among Saudi EFL learners. Participants were 53 undergraduates at the University of Jeddah, divided into an experimental group (n = 26) and a control group (n = 27). Over eight weeks, both groups engaged in two 1.5-hour writing sessions per week. The experimental group received WCF from ChatGPT, while the control group received human WCF. Pretests and posttests assessed participants’ grammatical competence before and after the intervention. Error-flagging accuracy was evaluated by a panel of experienced university teachers of English. Semi-structured interviews with six experimental-group participants explored perceptions of ChatGPT. Quantitative findings indicated that ChatGPT was a more effective source of WCF than human feedback, as the experimental group significantly outperformed the control group in the posttest. However, human feedback was slightly more accurate in error-flagging. Qualitative results revealed that most students valued ChatGPT for its precise error identification, though some considered human feedback clearer in explanation. The study concludes that ChatGPT can serve as a valuable supplementary tool for corrective feedback in Saudi EFL writing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".