Apprivoiser l’IA en enseignement postsecondaire : perspectives croisées des apprenants et apprenantes et du personnel enseignant au Nouveau-Brunswick
Bibliographic record
Abstract
Cette étude explore les perceptions et les pratiques liées à l'intelligence artificielle générative (IAg) dans l'enseignement postsecondaire au Nouveau-Brunswick (Canada).Basée sur une approche mixte, elle analyse les réponses de 281 participantes et participants issus de deux établissements d'enseignement.Les résultats montrent que l'adoption de l'IAg varie selon les profils, influençant les perceptions de son utilité et de ses implications éthiques.Tandis que les étudiants et étudiantes perçoivent l'IA comme un outil pédagogique, les enseignants et enseignantes expriment des préoccupations sur son impact.Ces divergences soulignent la nécessité d'une formation systématique pour développer une littératie de l'IA adaptée aux besoins du 21 e siècle.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".