Recension des usages d’intelligences artificielles génératives (IAg) pour offrir de la rétroaction en enseignement supérieur
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
L'intelligence artificielle gnrative (IAg) connat actuellement un essor sans prcdent en ducation.Parmi les potentialits de l'IAg pour favoriser l'apprentissage tudiant en enseignement suprieur, on retrouve sa capacit fournir une rtroaction personnalise.Pour comprendre l'tat de la recherche actuelle, cet examen de la porte synthtise les recherches traitant de l'usage d'IAg pour fournir de la rtroaction sur une production tudiante.Les rsultats montrent une varit d'IAg utilises et les caractristiques des rtroactions.La discussion souligne le besoin de mieux documenter les approches conceptuelles mobilises pour concevoir les IAg afin de favoriser la comprhension des tudes et le transfert des connaissances.
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.
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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it