AI and Higher Education: Navigating the New Frontier
Bibliographic record
Abstract
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.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".