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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".