Policy analysis of higher education institutes in Ontario, Canada: A focus on artificial intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examined the current state of academic integrity policies addressing the use of artificial intelligence (AI), specifically generative AI (GenAI), within publicly funded higher education institutions in Ontario, Canada. Amid the rapid proliferation of AI use across the sector, a regulatory gap persists at provincial and federal levels, contributing to varied institutional responses. Adapting Bretag et al.'s (2011a; 2011b) framework for policy analysis, we analyzed 19 academic integrity policies that reference AI and 1 standalone artificial intelligence policy. Our findings revealed a cautious and inconsistent sector-wide approach characterized by limited specificity, ambiguous responsibility, and limited support for AI competency development. Based on the findings synthesized in this review, we offer recommendations for AI policy and practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it