Évaluer à l’ère de l’IA : le paradoxe du double ancrage : Entre fractures numériques et retour aux fondamentaux pédagogiques
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’essor de l’intelligence artificielle (IA) générative reconfigure l’enseignement supérieur et interroge en profondeur les pratiques d’évaluation. Dans ce contexte, cet article propose une analyse réflexive des tensions entre compétences disciplinaires, littératie en IA et objectifs éducatifs. Plutôt que d’opposer interdiction et intégration de l’IA, il invite à repenser l’alignement entre finalités, méthodes et modalités d’évaluation, afin de former des étudiants capables d’un usage critique et éthique de ces technologies. Revenir aux fondamentaux pédagogiques apparaît comme un préalable pour préserver l’éducation comme espace d’émancipation dans un monde où l’IA est omniprésente.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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