Intelligence artificielle et formation universitaire : analyse bibliométrique des tendances et perspectives de recherche
Bibliographic record
Abstract
Cette étude examine les tendances et les développements des publications ainsi que la dynamique de collaboration scientifique entre auteurs, pays, organismes et sources récentes liés à l’utilisation de l’intelligence artificielle (IA) dans la formation et l’apprentissage universitaires. Une analyse bibliométrique de 285 articles publiés depuis 2014 jusqu’au 26 mars 2024, issus de la base de données Web of Science a révélé une forte association entre l’IA et des thèmes tels que l’éducation, la motivation des étudiants, le « feedback » et l’autocontrôle. La Chine et les États-Unis sont les pays les plus influents dans ce domaine de recherche, avec une collaboration croissante d’autres pays, comme le Afrique du Sud, Brésil, Canada, Israël, Pologne, Singapour, Vietnam depuis 2023. Les premières publications remontent à 2022 dans des revues spécialisées comme International Journal of Educational Technology in Higher Education et Educational Technology & Society. Bien que l’analyse présente certaines limites, telles qu’une compréhension réduite des tendances, une couverture partielle des publications et une faible représentativité des données, elle offre des insights précieux pour de futurs projets de collaboration interdisciplinaires et de recherches qualitatives visant à mieux comprendre la dynamique de l’intégration de l’IA dans l’enseignement supérieur.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.049 | 0.091 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".