Examen des perspectives des personnes étudiantes canadiennes de premier cycle concernant l'utilisation de l'IA générative pour l’apprentissage

Auteurs-es

DOI :

https://doi.org/10.21432/cjlt29124

Mots-clés :

intelligence artificielle générative, enseignement supérieur, recherche qualitative, apprentissage autorégulé

Résumé

L’émergence rapide des outils d’intelligence artificielle générative (IAg) présente à la fois de nouvelles opportunités et des nouveaux défis pour l’enseignement supérieur, toutefois, on en sait encore peu sur la manière dont les personnes étudiantes de premier cycle choisissent d’utiliser ces technologies. Cette étude a examiné les perspectives de personnes étudiantes canadiennes de premier cycle quant au rôle de l’IAg comme soutien à l’apprentissage tout au long des trois phases du cycle d’un cours magistral : avant, pendant et après le cours. À l’aide d’un sondage mixte (n = 296) nous avons analysé 118 réponses écrites par les personnes étudiantes à l’aide de l’analyse de contenu qualitative de Mayring et avons cartographié les thèmes dégagés avec le modèle d’autorégulation de l’apprentissage de Zimmerman. Les résultats indiquent que les personnes étudiantes conçoivent l’IAg comme un partenaire cognitif polyvalent qui les aide à se préparer avant les cours, à participer et à clarifier des points pendant les cours, et à réviser et avoir de l’aide avec les devoirs après les cours. Les personnes étudiantes ont également exprimé des préoccupations critiques liées à la dépendance excessive, à l’exactitude des réponses, à l’intégrité intellectuelle et à la protection des données, lesquelles correspondent aux vulnérabilités dans les processus d’autorégulation tels que le contrôle de soi, l’autoévaluation et la recherche d’aide. Les résultats mettent en évidence un changement conceptuel passant d’une conception institutionnelle de l’IAg comme outil de production à une conception étudiante de l’IAg comme mécanisme de renforcement des capacités intellectuelles. Nous soutenons qu’une intégration intentionnelle de l’IAg dans les pratiques pédagogiques et les politiques institutionnelles—alignée sur les sous-processus de l’autorégulation de l’apprentissage—peut favoriser une adoption responsable et éclairée par les personnes étudiantes. Cette étude apporte des données probantes et opportunes pour les personnes enseignantes et les responsables institutionnels qui naviguent entre les dimensions pédagogiques et éthiques de l’IAg dans l’apprentissage postsecondaire.

Bibliographies de l'auteur-e

Ann-Kathrin Grenz, Fraunhofer Institute for Applied Information Technology FIT

Ann-Kathrin Grenz is Researcher at Fraunhofer FIT in Germany, specializing in learning and generative AI. With a background in psychology and computer science, she explores how humans interact with intelligent systems and how AI can enhance education. Her work bridges technical innovation with evidence-based understanding of human behaviour. Email: Ann-kathrin.grenz@fit.fraunhofer.de ORCIDhttps://orcid.org/0000-0001-9849-4923

Soroush Sabbaghan, University of Calgary

Soroush Sabbaghan is Associate Professor at the University of Calgary and the inaugural Generative AI Educational Leader in Residence at the Taylor Institute for Teaching and Learning in Canada. His work focuses on the intersection of educational technology, teacher development, and equity, supporting inclusive and evidence-informed teaching practices for K–12 and higher education. Emailssabbagh@ucalgary.ca ORCID: https://orcid.org/0000-0002-2236-7693

Michele Jacobsen, University of Calgary

Michele Jacobsen is Professor of Learning Sciences at the University of Calgary in Alberta, Canada. Her research examines technology-enhanced learning, graduate supervision, and student experience in higher education. She co-leads the National Community of Practice on Supervision and publishes widely on online learning, graduate education, and the scholarship of teaching and learning. Emaildmjacobs@ucalgary.ca ORCID: https://orcid.org/0000-0002-0639-7606

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Publié-e

2026-01-16

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