Comparative analysis of artificial intelligence policies in universities across five countries
Bibliographic record
Abstract
Abstract The rapid integration of artificial intelligence (AI) in higher education has led to significant gaps in policy frameworks across universities worldwide. This study analyzes AI policies at 343 leading universities in Australia, Canada, China, the U.K., and the U.S., selected from the top 1500 institutions globally, as ranked by Times Higher Education for excellence in education across 2024/2025. Using a comparative analysis approach, we examined how these institutions govern the use of AI tools in teaching and learning contexts. Data were gathered from publicly available policy documents, student handbooks, and faculty guidelines, and analyzed using qualitative thematic coding supported by descriptive statistics. Our findings reveal diverse approaches, ranging from outright prohibitions to policies granting instructor's discretion, with notable regional differences influenced by cultural and regulatory factors. Quantitatively, for example, nearly half of universities adopted discretionary policies, while fewer than one in five issued outright bans. This study fills a gap in the literature by providing the first cross-regional analysis of AI policies in higher education, highlighting the absence of a universal framework. These insights offer valuable guidance for institutions to develop flexible, adaptive AI governance models that reflect their unique needs and values.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".