Comparing Artificial Intelligence Curricula in Canadian and US Universities
Bibliographic record
Abstract
Artificial Intelligence (AI) has impacted the world tremendously in the last decade, causing an increased demand for accessible AI education globally. Students benefit from studying AI earlier in the curriculum; however, AI courses can require a range of prerequisites, which can be structured differently in various educational contexts. In this paper, we study the curriculum structure of AI, Machine Learning (ML), and Data Science (DS) courses in Canadian Universities and compare it with that of US Research-1 institutions. There are many similarities between AI, ML, and DS courses in Canada and the US. For example, DS courses tend to be more accessible earlier in the CS curriculum compared to AI and ML. However, there are key differences between the two countries, with Canadian AI, ML, and DS courses generally being a part of a longer prerequisites chain, and Canadian CS departments offering fewer DS courses. Still, both Canadian and US institutions find innovative ways to introduce AI earlier in the curriculum, including via interdisciplinary courses and specialized courses with few prerequisites. This study corroborates earlier work in recognizing diversity in curricular frameworks in North America and recommends curricular revisions and early academic advising to ensure access to AI courses.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".