Raising University Students’ Critical Awareness of the Linguistic Limitations and the Potential Invalid Knowledge of ChatGPT Responses to Academic Writing Prompts
Why this work is in the frame
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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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it