L’intelligence artificielle en enseignement supérieur : étude exploratoire des perceptions, usages et inégalités d’adoption des étudiants et étudiantes
Bibliographic record
Abstract
Cette tude explore l'adoption et les usages de l'intelligence artificielle gnrative (IAg) par les tudiants et tudiantes de l'enseignement suprieur (N = 756). travers une approche mthodologique mixte, l'enqute rvle des disparits significatives dans l'adoption de l'IAg, influences par le domaine disciplinaire, le genre et le niveau d'tudes.L'tude met galement en vidence les bnfices perus, notamment en matire de personnalisation de l'apprentissage et d'organisation du travail, tout en soulevant des questions thiques et des risques de dpendance.Les rsultats appellent des stratgies adaptes pour une intgration quilibre et quitable de l'IAg dans l'enseignement suprieur.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".