U.S. higher education institutions are sharply expanding undergraduate majors, minors and specializations in artificial intelligence. According to the Center for Inclusive Computing at Northeastern University, there were at least 1,000 AI programs across nearly 584 American colleges and universities as of April; that total includes 78 majors and 103 minors. By comparison, The New York Times reported that in 2021 only five schools offered AI majors.
Wide variation in curricula: theory, practice, interdisciplinarity
Bachelor’s degree requirements in AI vary considerably. Some programmes are technically and mathematically intensive, while others take an interdisciplinary approach that includes ethics, policy or domain-specific applications. Some focus on theoretical foundations of AI; others prioritize how to build, deploy and operate AI systems in practice.
Carnegie Mellon University was the first U.S. university to introduce a bachelor’s degree in artificial intelligence in 2018. Its curriculum emphasizes mathematical rigor: it requires seven mathematics and statistics courses, five computer science and principles/programming courses, three artificial intelligence courses, one ethics course, and additional classes covering human cognition, perception and language, machine learning, and human–computer interaction.
By contrast, the University of Oklahoma Polytechnic Institute’s applied AI degree stresses practical skills. Beyond math and statistics requirements, students must complete 15 AI and computing courses on subjects such as robotics, machine learning, reinforcement learning, computer vision, cloud computing and DevOps.
Other programs are deliberately interdisciplinary. Drake University in Iowa offers a Bachelor of Arts in AI aimed at humanities and business students; its requirements are flexible and let students choose from clusters in philosophy, English, computer science, information systems and psychology. That degree requires only two math courses.
Many institutions that do not offer standalone AI degrees still provide specialized concentrations. Students on the AI track at Stanford University take seven qualifying courses in areas like natural language processing, computer vision and robotics. (Disclosure: Andrew Ng serves as an adjunct professor in Stanford’s Computer Science department.)
Debate and concerns
Commentators differ on whether universities have kept pace with employer expectations for AI competency; some say institutions were too slow, while others dismiss AI degrees as a passing fad. Even proponents caution that narrowly specialized AI degrees could undermine a broader computer science foundation that students may need to adapt in a rapidly changing field.
Why this matters
Current curricula will influence who enters the AI profession and which skills new engineers bring. AI is a heterogeneous field: some roles resemble traditional software engineering with AI components, while others demand deeper machine learning research, distributed systems or data engineering expertise. Programs will also vary in whether they prepare students for graduate study or assume the bachelor’s is a terminal qualification.
Editorial perspective
AI is evolving so rapidly that many universities struggle to adapt their traditional curriculum-change processes—where faculty learn a topic, propose courses, obtain committee approvals, and revise degree requirements—to the field’s pace. Still, it is a positive sign that many innovative faculty and administrators are finding ways to move faster; those efforts will help prepare students not only for the jobs of 2026 but also for roles that will appear years from now.
