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Comparing Artificial Intelligence Curricula in Canadian and US Universities
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Replicates a US prerequisite-structure analysis of AI, ML, and DS courses on 30 sampled Canadian CS departments, comparing exposure levels, prerequisite chains, and clusters across the two countries.
Problem
How AI, ML, and DS course prerequisites are structured in Canadian universities and how accessible these courses are early in the CS curriculum compared with US R1 institutions.
Approach
Applies a previously developed prerequisite-graph framework (exposure level, Sankey diagrams, k-means clustering over prerequisite features) to Canadian CS departments and contrasts the results with the US R1 analysis, identifying low-exposure course designs such as interdisciplinary intro AI courses and parallelized prerequisites.
Key results
- Canadian AI courses most commonly have exposure level 3 versus level 2 for US AI courses; Canadian ML courses most commonly level 4 versus 2 in the US.
- Canadian DS courses had lower exposure and fewer DS courses were offered overall; 91 AI-related courses (36 AI, 39 ML, 16 DS) were collected from 30 universities.
- 26 of 36 Canadian AI courses (72%) require Data Structures; Canadian universities commonly require an intro mathematical proof course in first year, which the US sample lacked.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Canadian rather than US prerequisite structures | AI courses · Canada vs US R1 institutions | most common exposure level: 2 (US) -> 3 (Canada) | Hurt |
| Use Canadian rather than US prerequisite structures | ML courses · Canada vs US R1 institutions | most common exposure level: 2 (US) -> 4 (Canada) | Hurt |
| Offer DS courses as an early curriculum entry point | AI and ML education · Canadian and US curricula | exposure level | Helped |
| Parallelize CS and mathematics prerequisites | Intermediate AI and ML courses · Canadian and US curricula | exposure level | Helped |
| Offer introductory AI courses with few or no prerequisites | AI education · Canadian and US curricula | exposure level | Helped |
| Consolidate mathematics prerequisites into an early ML course | ML education · Canadian curricula | exposure level | Helped |
| Require Data Structures for AI courses | Canadian AI courses · Canadian universities | courses requiring Data Structures: 26 AI courses (72%) | Hurt |
| Offer fewer DS courses | Canadian CS departments · Canada vs US | number of DS courses | Hurt |
Surprising findings
- Canadian universities often place OOP after CS1 or CS2, whereas US universities typically use OOP as an introductory first-year course.
Limitations
- Relies on publicly available course calendars, which may be incomplete or outdated and may not specify strict prerequisite enforcement.
- Only CS departments were analyzed; AI/DS courses in Statistics, Mathematics, or disciplinary departments were excluded.
- Quebec's extra pre-university year was not modeled, likely underestimating exposure levels there; clustering used fewer features than the US analysis, making comparison approximate.
Experimental setup
Datasets: Canadian university CS course calendars (30 sampled universities, 91 AI/ML/DS courses) US R1 course data from Niousha et al. 2024
Metrics: exposure level Cohen's kappa inter-rater reliability silhouette score
Builds on: Niousha et al. 2024 US R1 AI prerequisite analysis
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