L'IA peut-elle nous dispenser de l'effort d'apprendre ?
Bibliographic record
Abstract
L’intelligence artificielle (IA) suscite autant d’espoirs que d’inquiétudes dans le domaine de l’éducation. D’un côté, elle offre des outils puissants pour accompagner l’apprentissage, par exemple, en ajustant des exercices aux performances de chaque élève en maths, comme le font DreamBox ou Adaptiv’Math, ou en adaptant l’apprentissage des langues à l’âge de l’apprenant, comme Duolingo. Mais elle peut aussi favoriser la paresse intellectuelle : les IA actuelles ne se contentent plus de fournir des pistes ou des chiffres comme les moteurs de recherche ou les calculatrices, mais produisent directement des contenus complets – résumés, essais, emails, codes informatiques – à la place de l’élève. Cette délégation excessive des tâches cognitives ouvre la possibilité d’une réduction de l’engagement dans la formulation des idées, la réflexion et la régulation du processus de production intellectuelle.
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 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".