Raising University Students’ Critical Awareness of the Linguistic Limitations and the Potential Invalid Knowledge of ChatGPT Responses to Academic Writing Prompts
Bibliographic record
Abstract
As ChatGPT becomes widespread globally, university students utilize its responses to develop their academic writing assignments. In the meantime, studies have shown linguistic limitations and invalid information in ChatGPT responses. This study aims to raise university students’ critical awareness of ChatGPT limitations in academic writing by using a researcher-designed critical review activity. This study follows a quasi-experimental method that instructs students on how to evaluate ChatGPT academic writing responses. Students were required to practice a critical review activity to evaluate and criticise the linguistic appropriateness and knowledge credibility of the ChatGPT responses. The research participants included 120 university students enrolled in an Academic Writing course at the University of Prince Edward Island, Cairo campus. The academic writing course was taught for three months; meanwhile, students practised the designed critical review activity to evaluate the linguistic features and credibility of the ChatGPT responses. Pre and post-critical awareness questionnaires were administered to measure the difference in students’ critical awareness of the ChatGPT Limitations. The findings showed that participants’ critical awareness during the pre-critical awareness questionnaire was poor. However, in the post-critical awareness questionnaire, the critical awareness of most of the participants was satisfactory. Therefore, the study confirms that integrating critical review activities in the academic writing syllabus is crucial to raising students’ critical awareness towards ChatGPT Limitations. The study's findings provide a foundation for creating suitable instructional materials to integrate ChatGPT properly in teaching Academic Writing Courses.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".