L’intelligence artificielle générative (IAg) dans l’apprentissage du FLE chez l’étudiante et l’étudiant marocains : étude de cas de la Faculté des sciences de Rabat
Bibliographic record
Abstract
L'usage informel de l'intelligence artificielle générative (IAg) semble transformer l'expérience d'apprentissage des étudiantes et étudiants marocains, d'où l'importance d'analyser leur agir pour comprendre l'impact de cette technologie sur leur rendement.Les réflexions d'Holec (1981) sur l'autonomie, enrichies par Benson (2013), serviront de cadre pour cette étude dont l'objectif est de déterminer la contribution de l'IAg dans l'apprentissage du français langue étrangère (FLE) en mode hybride.À cette fin, une méthodologie de recherche mixte auprès d'étudiants et étudiantes de la Faculté des sciences de Rabat (FSR) est privilégiée.Les résultats montrent que l'IAg, fort présente dans les pratiques étudiantes, soutient l'expérience de co-construction des compétences linguistiques ainsi que le développement de l'autonomie et de l'esprit critique et éthique d'un grand nombre de personnes apprenantes qui l'utilisent de manière réfléchie.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".