Comparing Artificial Intelligence Curricula in Canadian and US Universities

Source publication / research team or educational organization described in paper

United States, Canada

Researchers, educators, instructors, or facilitators as described in the source publication

• Higher education

Course implementation or course design

5 hours of Basic Machine Learning (Servin et al

Not specified in extracted text

Computer vision / image classification, ML concepts / supervised learning

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Higher education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Artificial Intelligence (AI) has impacted the world tremen- dously in the last decade, causing an increased demand for ac- cessible AI education globally.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Computer vision / image classification, ML concepts / supervised learning

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design.
• AI capability focus: Computer vision / image classification, ML concepts / supervised learning.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Instructional / curriculum-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• In this paper, we study the curriculum structure of AI, Machine Learning (ML), and Data Science (DS) courses in Canadian Universities and com- pare it with that of US Research-1 institutions.

• In this paper, we study the curriculum structure of AI, Machine Learning (ML), and Data Science (DS) courses in Canadian Universities and com- pare it with that of US Research-1 institutions.

Artificial Intelligence (AI) has impacted the world tremen- dously in the last decade, causing an increased demand for ac- cessible AI education globally. Students benefit from study- ing AI earlier in the curriculum; however, AI courses can re- quire a range of prerequisites, which can be structured differ- ently in various educational contexts.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Curriculum / course design
• Computer vision / image classification
• ML concepts / supervised learning

• Completed

Higher education

Higher education

Computer vision / image classification, ML concepts / supervised learning

Instructional / curriculum-based learning

Low to Medium

Medium

Comparing Artificial Intelligence Curricula in Canadian and US Universities

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

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35176

https://ojs.aaai.org/index.php/AAAI/article/view/35176

https://ojs.aaai.org/index.php/AAAI/article/view/35176/37331

013_Comparing Artificial Intelligence Curricula in Canadian and US Universities.pdf

9

Artificial Intelligence (AI) has impacted the world tremen- dously in the last decade, causing an increased demand for ac- cessible AI education globally. Students benefit from study- ing AI earlier in the curriculum; however, AI courses can re- quire a range of prerequisites, which can be structured differ- ently 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 com- pare 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 acces- sible 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 insti- tutions find innovative ways to introduce AI earlier in the cur- riculum, including via interdisciplinary courses and special- ized 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.

• Higher education

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• group_size

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.