Comparing Artificial Intelligence Curricula in Canadian and US Universities

Rose Niousha, Lexie Jingruo Guo, Rick Kaifeng Li, Narges Norouzi, Lisa Zhang

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.