Évaluer à l’ère de l’IA : le paradoxe du double ancrage : Entre fractures numériques et retour aux fondamentaux pédagogiques
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.
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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 | 0.034 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".