Des hypothèses pour concevoir des tâches permettant aux étudiants et étudiantes d’évaluer la pertinence des textes générés par les IA
Bibliographic record
Abstract
La mise à la disposition du grand public des IA génératives constitue un défi pour l’enseignement supérieur. Pour faire face à cette situation, nous proposons de concevoir des tâches permettant aux étudiants et étudiantes d’évaluer la pertinence des textes générés par les IA. Nous le faisons en nous engageant dans un processus itératif de conception qui s’inscrit dans une méthode de recherche basée sur la conception. Ce processus nous permet d’expliciter et de tester nos hypothèses de conception. Celles-ci portent notamment sur les conditions de viabilité des configurations d’activités collectives en formation et sur l’incidence de toute technique – et ici de la technique des IA génératives – sur la cognition humaine.
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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 | 0.051 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".