EFL Students’ Perceptions on Using ChatGPT as an AI Tool for Developing Academic Writing Skills: A Case Study at University College of Haql
Bibliographic record
Abstract
With advancing artificial intelligence (AI) technology, writing assistance tools now include plagiarism detectors, ChatGPT and grammar checkers. These AI tools help students of EFL to improve their writing, communication, creativity, and critical thinking, thereby developing language skills and academic success. Various writing tasks are supported by the Open AI Chat GPT application, and this study explores the perceptions of students regarding its effectiveness for improving their skills in academic writing. Using a closed-ended questionnaire with Likert scale for the collection of data, the quantitative study involved a sample of 50 students from the English language program within the University College of Haql, Saudi Arabia, who had used ChatGPT during one semester. Findings show a majority of respondents held a positive view of their use of ChatGPT for increasing motivation to learn (42.5%), provision of user-friendly and comprehensive features (43.5%), and assisting in correction of grammatical errors (33.3%). Some respondents were neutral with regard to the ability of ChatGPT to offer explanations for improving skills in writing (33.5%). To conclude, ChatGPT is able to serve as a tool of value to help students enhance their writing skills in English, though there is potential for further refinements to be made that would offer explanations in greater detail.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".