Computer science enrollment is falling at U.S. four-year institutions even as colleges broaden access to artificial intelligence education across their campuses. The two developments point to a changing academic landscape: AI-related study is increasingly available to students in psychology, music, biology and other fields without requiring them to pursue a traditional computer science major.
In brief
- Computer and information sciences enrollment fell at U.S. four-year institutions while AI education expanded beyond computer science departments.
- Universities are using different models, from AI majors and minors to discipline-specific fluency programs and short interdisciplinary courses.
- The changes broaden access to AI study but do not replace the technical foundations taught in computer science programs.
Undergraduate enrollment in computer and information sciences at four-year institutions fell 8.4% in spring 2026 from spring 2025. The decline followed a 3.6% drop in fall 2025, while graduate enrollment in the field fell 14% during that fall term. The figures, reported by Fortune, describe enrollment in a specific field and type of institution, not a broad loss of interest in technology.
At the same time, universities are adding AI majors, minors, courses and literacy initiatives aimed at students whose main subject lies outside computer science. The resulting options vary widely. Some programs focus on technical foundations, while others place AI in the context of a student’s discipline, including questions about how the technology is used and where its limits lie.
Enrollment changes arrive as AI education moves across campus
Hiring for entry-level software developers has cooled, and some work associated with junior developers is increasingly handled by AI agents. Those shifts form part of the environment facing students as they choose an academic path, as reported by the Los Angeles Times. They do not, however, establish a single reason for the decline in computer science enrollment.
What is visible is a broader distribution of AI teaching. Institutions are creating ways for students to gain familiarity with AI without following the full sequence of courses traditionally associated with a computer science degree. That approach can connect technical questions with work in other disciplines while preserving more specialized programs for students seeking deeper computing training.
More routes for students outside computer science
Northwestern University is adding an AI major and simplifying prerequisites so students outside computer science can more easily enroll in AI or machine-learning courses. The university already offers an AI minor. The changes make room for students who may want to apply AI concepts in another area of study without entering a conventional computer science curriculum.
The expansion reflects a wider effort to bring AI instruction into fields that have their own methods and questions. The relationship between computing and research is also part of the broader science landscape, where technical tools increasingly intersect with work across disciplines. In higher education, that can mean teaching AI in ways that relate to a particular subject rather than presenting one uniform course of study for every student.
Ohio State University has adopted a requirement-based model. Its AI fluency initiative applies to students beginning with the class of 2029, with the expectation that they will be fluent in AI related to their major by graduation. The initiative does not make every student a computer science specialist. Instead, it frames AI knowledge around the context in which a graduate may encounter the technology.
Universities are building AI programs at different scales
The University of Florida offers more than 200 AI-related courses across its 16 colleges, with 14,000 students enrolled annually. The university also says that 71% of graduates in its spring 2026 commencement cycle took at least one AI course during their studies. The figures show how extensive an institution-wide approach can become, but they do not suggest that colleges nationwide are using the same model or reaching the same number of students.
Colby College has taken a smaller interdisciplinary approach. Its eight-week program brought together 30 students representing 23 majors or intended majors, with no previous programming experience required. Students learned AI fundamentals, examined ethical questions and worked in teams spanning different academic backgrounds. The format offered an introductory route into the subject while allowing participants to connect it with other interests.
Programs such as these create alternatives to the assumption that technology education must be contained within a single department. They also show that AI study can take different forms: a major designed around the field itself, a minor for students in another discipline, a graduation expectation tied to a major or a short program that introduces shared concepts.
Course development is accelerating at some campuses
The rapid pace of AI development is affecting how universities build courses. At Virginia Commonwealth University, the time needed to design and approve a course has fallen from about a year and a half to months and, in some cases, weeks. Faster approval can let institutions respond more quickly to a changing subject, but it also means faculty must decide what belongs in a new course as the tools and questions surrounding them continue to evolve.
For students, the growing number of AI offerings means that a course label may describe very different kinds of learning. A program can focus on building technical knowledge, applying AI within a field, or examining issues such as ethics, copyright and misinformation. These approaches can overlap, but they serve different educational purposes.
Computer science remains central to the broader mix of options. Programming expertise is still needed for engineers, including the ability to recognize when AI systems produce errors. The expansion of AI courses outside computer science therefore does not make technical foundations unnecessary. It changes who can study AI-related tools, how those studies connect to other disciplines and how colleges organize instruction for students with different goals.
The enrollment data shows a decline within computer and information sciences at four-year institutions. The expanding course offerings show a separate but related response: universities are opening multiple paths into AI education rather than relying on a single model of technical training.
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