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Felix Muzny

Publications and source records attributed to Felix Muzny.

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A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors

In this work, we locate and analyze existing undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026, creating a historic record at a time of great change in this area. To create this record, we developed a tool to detect, scrape, and display data from 361 undergraduate AI programs--majors, minors, concentrations, and certificates--at 4-year universities. Our tool, available at https://cicmap.ai, searched 563 institutions to locate these programs, a sample that represents 87% of all undergraduate Computer Science (CS) graduates in the U.S in 2025. This tool allows prospective students, guidance counselors, administrators, and faculty to easily access AI program requirements and is designed to continually update as new programs emerge. To the best of our knowledge, this survey represents the most comprehensive snapshot of the state of AI programs in the U.S. to date. With this work we offer three important contributions: 1) a record of AI programs in the U.S. at a time of great upheaval; 2) a tool to explore AI programs and their requirements; and 3) an analysis of the courses required for 66 AI majors and 87 AI minors. Our analysis of majors and minors shows great variability in the size and the requirements of these degrees, but we note two takeaways. First, not all majors require a general AI course, but if they don't, they do require a Machine Learning (ML) course. Second, more than a third of majors require an Ethics in AI course but only 24% of minors do.

cs.CY

The Effectiveness of Embedded Values Analysis Modules in Computer Science Education: An Empirical Study

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern's program that embeds values analysis modules into CS courses. The resulting data suggest that such modules have a positive effect on students' moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.

cs.CY