Université et intelligence artificielle : entre promesses pédagogiques et vigilance éthique
Bibliographic record
Abstract
Abstract: This study explores how Artificial Intelligence (AI) is reshaping higher education by transforming teaching, learning, and institutional management. The main objective is to examine how universities can leverage AI’s pedagogical potential while addressing its ethical implications. Using a qualitative methodology combining a literature review and the analysis of international case studies, the research identifies concrete applications of AI in university contexts, such as adaptive learning systems, automated assessment, and educational chatbots. The findings reveal that AI can enhance student engagement and instructional efficiency, but also raise concerns about algorithmic bias, data privacy, and digital inequality. The article concludes that a responsible and gradual integration of AI— supported by ethical guidelines and continuous teacher training—is essential to ensure that technology strengthens, rather than replaces, the humanistic mission of higher education. Résumé : Cette étude analyse la manière dont l’intelligence artificielle (IA) redéfinit l’enseignement supérieur en transformant les pratiques pédagogiques, la gestion institutionnelle et la mission éducative de l’université. L’objectif principal est d’examiner comment les établissements peuvent exploiter le potentiel éducatif de l’IA tout en répondant à ses enjeux éthiques, sociaux et méthodologiques. En combinant une analyse documentaire et l’examen critique de cas observés dans plusieurs universités étrangères - notamment au Canada, en Europe et en Asie -, la recherche met en évidence des usages concrets tels que les systèmes d’apprentissage adaptatif, l’évaluation automatisée et les agents conversationnels éducatifs. Les résultats révèlent que l’IA peut renforcer l’engagement des étudiants et améliorer l’efficacité institutionnelle, mais qu’elle exige parallèlement une vigilance accrue quant à la protection des données, aux biais algorithmiques et à la fiabilité des outils de détection du plagiat. L’article plaide ainsi pour une intégration progressive et responsable de l’IA, soutenue par une formation continue des enseignants et un encadrement éthique rigoureux, afin que la technologie demeure un instrument au service de la mission humaniste de l’université.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".