Undergraduate computer and information sciences enrollment at four-year institutions fell 8.4 percent this year; some researchers have suggested AI anxiety and a difficult entry-level tech market are contributing to the decline.
Enrollment Reversals and Classroom Capacity Pressures at CUNY
The City University of New York is experiencing a sharp contraction following a decade-long computer science boom. A new report from the Center for an Urban Future found that enrollment surged by 146 percent from 2014 to 2024, even as overall university enrollment declined.
That trajectory has reversed. University system data shows that computer science enrollment fell 15 percent between 2024 and 2025, dropping from 10,330 to 8,819 students even while CUNY’s total enrollment increased. At the same time, entry-level technology jobs in New York City have fallen by 49 percent since 2022.
Eli Dvorkin, editorial and policy director at the Center for an Urban Future, noted that the decade of enrollment growth left the institution confronting a serious structural hurdle regarding instructional capacity.
“Just a few years ago, a computer science degree alone could take a student pretty far, but in today’s much tougher entry-level job market, students need more applied skills, exposure to AI in context, real-world projects, industry connections, and faculty who have the insights to help them connect to careers and enough time to mentor them.”
Faculty growth failed to keep pace with the student boom. At Queens College and several other campuses, student-to-faculty ratios more than doubled.
Ping Ji, chair of the computer science department at Hunter College, stated that the campus continues to face high ratios close to 50 to one, well above CUNY’s target STEM ratio of roughly 14 to one. Ji explained that students remain motivated despite the bottlenecks, noting, They are thirsty for knowledge, but where do we put them?
Industry Alignment and the Practical Realities of Programming Languages
The disconnect between academic instruction and corporate hiring requirements presents another obstacle for graduates entering the workforce. Recent alumni recall packed lecture halls where individual interaction with professors was nearly impossible.
Oluebube Onwuamaegbu, who graduated from Hunter College in 2025 and now works as a software engineer for Amazon, recalled taking an introductory Python course with nearly 800 students. She noted that many of her courses emphasized C++ despite prospective employers often seeking experience with other programming languages.
“In any of the interviews I’ve done, I have never had someone prefer C++. Luckily, I did end up getting something, but a lot of places didn’t want to interview you on knowing C++ alone.”
The mismatch reinforces findings that universities need more faculty with applied expertise and industry experience to match the evolving skills demanded by technology employers.
Historical Debates Over the Definition of Computer Science
The current struggle to define the proper educational approach echoes decades of philosophical debate among pioneers of the discipline. Computer scientist Edsger Dijkstra famously dismissed certain programming languages, once calling Fortran the infantile disorder
and asserting that the use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offence.
Dijkstra is associated with the notion that computer science is fundamentally theoretical rather than tool-dependent, an idea captured in the maxim that computer science is no more about computers than astronomy is about telescopes.
That view faced pushback from early system builders. In a 1967 letter to the journal Science, Allen Newell, Alan Perlis, and Herbert Simon argued that artificial phenomena created by engineered systems deserve rigorous scientific study.
In 1974, Donald Knuth offered a distinct view by defining computer science as the study of algorithms rather than computers themselves. Yet as universities confront declining enrollment, tighter labor markets, and the disruptive arrival of artificial intelligence, modern institutions must balance theoretical foundations against the immediate practical needs of students entering a tightening workforce.
