Rose Niousha

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2024-234

December 20, 2024

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-234.pdf

This work examines Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS) courses in North America, focusing on the accessibility of the courses to learners of different academic backgrounds. Analyzing 50 US and 30 Canadian universities, it identifies key differences in course pathways. DS courses generally have lower entry requirements, while AI and ML courses in both countries often demand extensive prerequisites. US institutions typically provide earlier access to AI, ML, and DS courses with more flexible prerequisites, whereas Canadian universities emphasize more layered preparation, delaying student exposure. To improve accessibility, the study highlights several strategies such as parallelizing prerequisites, integrating foundational content into introductory courses, and offering low-barrier interdisciplinary courses to engage students early. The thesis introduces a codebook and exposure-level metric to systematically analyze and compare curricula across different institutions. These findings provide actionable recommendations to make AI in Higher Education more accessible and prepare a diverse range of students for opportunities in this rapidly growing field.

Advisors: Narges Norouzi

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BibTeX citation:

@mastersthesis{Niousha:EECS-2024-234,
    Author= {Niousha, Rose},
    Title= {Artificial Intelligence Curricula: Comparative Prerequisite Pathways Analysis in North America},
    School= {EECS Department, University of California, Berkeley},
    Year= {2024},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-234.html},
    Number= {UCB/EECS-2024-234},
    Abstract= {This work examines Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS) courses in North America, focusing on the accessibility of the courses to learners of different academic backgrounds. Analyzing 50 US and 30 Canadian universities, it identifies key differences in course pathways. DS courses generally have lower entry requirements, while AI and ML courses in both countries often demand extensive prerequisites. US institutions typically provide earlier access to AI, ML, and DS courses with more flexible prerequisites, whereas Canadian universities emphasize more layered preparation, delaying student exposure. To improve accessibility, the study highlights several strategies such as parallelizing prerequisites, integrating foundational content into introductory courses, and offering low-barrier interdisciplinary courses to engage students early. The thesis introduces a codebook and exposure-level metric to systematically analyze and compare curricula across different institutions. These findings provide actionable recommendations to make AI in Higher Education more accessible and prepare a diverse range of students for opportunities in this rapidly growing field.},
}

EndNote citation:

%0 Thesis
%A Niousha, Rose 
%T Artificial Intelligence Curricula: Comparative Prerequisite Pathways Analysis in North America
%I EECS Department, University of California, Berkeley
%D 2024
%8 December 20
%@ UCB/EECS-2024-234
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-234.html
%F Niousha:EECS-2024-234