AI and Higher Education: Navigating the New Frontier
Notice bibliographique
Résumé
With increasingly powerful generative artificial intelligence (AI) tools now widely available, post-secondary institutions are struggling to keep up with a rapidly evolving landscape. While much has been discussed about the potential impact of this technology, there is still limited empirical data on how students, educators, and administrators are integrating AI in teaching and learning contexts. In this presentation, we share key findings from a multi-methods study conducted by The Conference Board of Canada, on behalf of the Future Skills Centre (FSC). The study includes a national survey of postsecondary students (N=2,401) and educators (N=402), as well as interviews with individuals leading responses to AI in higher education institutions (N=42). We found that frequent usage was not widespread among students, with 20 per cent of students reporting using generative AI most or all of the time. Usage varied significantly across students in different sociodemographic groups and fields of study. Power users – those who report using generative AI most or all of the time – had similar levels of concern as non-users about the potential drawbacks of generative AI, despite having more favourable attitudes toward its use. We also found an association between frequency of use and better learning experiences and outcomes, but the mechanisms and conditions under which this occurs need to be further investigated. Among educators, we found that most have neither explicitly permitted nor prohibited student use of AI tools. Notably, 80% reported not receiving any formal guidance or training from their institutions. There is a strong demand for professional development in this area, with educators seeking training for both themselves and their students. Educators who use generative AI more frequently tend to be more optimistic about its potential, although they remain wary of its ethical implications and possible threats to the integrity and reliability of knowledge. Conversations with institutional leaders revealed a wide range of perspectives on AI, from views that it has radically transformed the role of the teacher, to skepticism about its overall impact. Many leaders expressed enthusiasm for AI as a tool for enhancing higher-order learning. These findings have important implications for various post-secondary stakeholders, particularly instructors and administrators looking to integrate AI into educational environments. We conclude with recommendations focused on fostering critical literacy, ensuring transparency and accountability in AI use, and promoting equity in access to AI tools and training.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,009 | 0,042 |
| Communication savante | 0,022 | 0,037 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».