Exploring ChatGPT’s Application of Effective Writing Feedback in EFL Context: Teachers’ Perceptions
Bibliographic record
Abstract
Recent research in language education has focused on evaluating the efficacy of ChatGPT in language acquisition. This study explored the accuracy, clarity, and effectiveness of ChatGPT feedback and its effectiveness as an Automated Essay Scoring tool. Experienced EFL instructors were selected to identify the strengths and limitations of ChatGPT by comparing it to established criteria for writing feedback. The findings indicated that ChatGPT can support educators in delivering clear, constructive, and efficient feedback to students, particularly in the domains of grammar, vocabulary, and mechanics, as well as highlighting positive and negative aspects of students’ writings. However, instructor intervention remains necessary for addressing organizational and content-related errors. Moreover, unlike human instructors, ChatGPT appears to lack sensitivity to contextual factors such as learner interaction and proficiency level. This research endeavors to inform teachers, educators, artificial intelligence developers, and policymakers about the utility of ChatGPT; including its advantages and limitations, as a writing correction tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".