Étude de l’adoption de l’intelligence artificielle par des personnes enseignantes du postsecondaire au Québec en fonction de cinq types d’usages
Bibliographic record
Abstract
Cette tude vise expliquer l'adoption de cinq types d'usages de l'IA par les personnes enseignantes du postsecondaire : prdiction de la russite, rtroaction, dtection du plagiat, cration de matriel et valuation.Des personnes enseignantes du postsecondaire (n = 127) se sont prononces sur les facteurs d'attitude, de performance perue, de facilit d'utilisation et d'anxit, de mme que sur des facteurs de littratie de l'IA (technique, pdagogique et thique).Des modles d'quations structurelles ont t estims pour expliquer l'intention d'utilisation.Il ressort des principaux rsultats que des connaissances techniques sur l'IA sont associes des attentes de performance plus faibles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".