ChatGPT: Hero or Villain? Comparative Evaluation by Canadian High School Students and Teachers
Bibliographic record
Abstract
Background: Since its release at the end of 2022 ChatGPT use has become very popular for students and teachers; however, there are no clear instructions on whether it should be banned or allowed for high school-related activities. We assessed the extent, areas, and expectations of current ChatGPT use and evaluated concerns viewed by students and teachers in high school. Methods: Two surveys for high school students and teachers were created. Informed consent obtained from all participants before they completed the survey. Results: Total of 165 responses from students and teachers were analyzed. Both groups have similar familiarity (~80%) with ChatGPT. Study assistance was the most agreeable domain for all groups. Only about 10% viewed that ChatGPT should be banned. Reduced critical thinking ability and learning motivation; inaccuracy output, ethical dilemmas were viewed as the most common ChatGPT disadvantages. Most students and teachers agreed on <25% of ChatGPT allowance in school-related tasks. 40-50% of students and teachers viewed the need for guidelines on proper ChatGPT use. Conclusions: ChatGPT can be both a hero and a villain. There is an urgent need to have clear school ChatGPT guidelines and teaching its correct use in high school setting.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".