Mapping the Pathways: A Comparative Analysis of AI/ML/DS Prerequisite Structures in R1 Institutions in the United States

This Research Full paper focuses on the challenges in artificial intelligence, machine learning, and data science education—referred to as “artificial intelligence” courses here-after-often characterized by extensive prerequisites that limit student access. We analyze the course structures and prerequisites of these courses in computing departments at 50 Research-1 institutions in the United States, recognized for their “Very High Research Activity.” Our methodology involves analyzing course syllabi to examine the structure and prerequisites of these courses, using open coding to develop a unified codebook to identify prerequisites and determine the earliest exposure levels for students. A clustering analysis was also conducted to identify common and differing curriculum approaches among institutions. Results show that data science courses require less initial exposure, while artificial intelligence and machine learning courses require more prerequisites. Standard requirements for artificial intelligence courses include basic data structure (Computer Science 2) and algorithms, with machine learning courses requiring more mathematics preparation. Moreover, public institutions offer advanced courses with more prerequisites compared to private institutions. Overall, this study recognizes considerable diversity in curricular frameworks across Research-1 institutions and encourages institutions to revise curricula to broaden access to artificial intelligence education and increase participation in research.