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βThe reality, for better or worseβ: Columbia comp sci students and faculty grapple with AIβs disruption of the field
βThe reality, for better or worseβ: Columbia comp sci students and faculty grapple with AIβs disruption of the field βThe reality, for better or worseβ: Columbia comp sci students and faculty grapple with AIβs disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS β29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widelyβeven within the same districtβand some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home stateβs Department of Education, which she said she found βoutdated and inaccessible.β Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbiaβs undergraduate schools, computer science has become the most popular majorβaccounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the majorβs popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. βAnecdotally, people are moving away from CS as a major,β Daniel Bauer, a senior lecturer in the computer science department, said. βTheyβre still taking some of our classes, right, because they think they need that, but theyβre instead majoring in other things.β Bauer described a field that had shifted from what he called a βnerdy outlier disciplineβ to one that βeveryone needs,β a sign that technical skills have become a baseline expectation across industries with the emergence of AI. βA lot of people thought, βOh, itβs a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,ββ Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls βcode monkey jobs,β roles where a large number of employees are βjust writing out code nine-to-five.β Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. βThings kind of started to really pick up in 2014, 16,β he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where βstudents were getting more experience outside of class than they were inside of classβ through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS β27, said that during a recent internship in Big Tech, she saw that βevery single tech company was racing to integrateβ large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS β29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. βA lot of companies outright say if youβre not using coding in your job, you might get fired for not being productive enough,β Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also βmake use of it to maximize their productivity.β Richard Li, CC β28, described a βfranticnessβ among his peers as the pace of change accelerates. He explained how βsome new toolβ is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesnβt need to βmatch that speed all the timeβ and keep up with every development. Li has learned that AI tools allow him to βtake a breathβ and βget back into the loop much easier than beforeβ when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. βYou canβt just get your degree and stop learning after that. You have to always be learning,β Yu said. βThat is something you basically have to expect for your job.β AI has not only affected studentsβ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a studentβs grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. βThe assumptionβespecially among pessimistsβis that theyβre using it to do their homework,β Murphy said. βThey take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.β Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students werenβt using the technology βnaΓ―ve,β and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. βThose are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,β he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. βPeople who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,β he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is βcoordinatedβ across the department, βnot course-by-course,β with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to βread, verify, and reason about code, whether they or a model wrote it.β Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to βguide inquiry rather than supply answers,β the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEASβ new AI minor, which it announced in November 2025, reflects the Universityβs attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide βessential AI knowledgeβ while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbiaβs largest computer science clubs. βAs people start to ask AI for questions that they would normally ask students and members, it starts to isolate peopleβ Li said. Alexa Kafka, BC β27, echoed Liβs sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasnβt been to office hours βin at least a year.β Bauer added that faculty are exploring long-term solutions to the assessment problem. βOne of the things that weβre trying to do at the department or school level is to maybe move towards computerized testing facilities,β Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. βWe have to accept that this is the reality, for better or worse.β But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. βCode is so variable and can be approached in so many directions, thereβs never just going to be one correct answer. There can be multiple optimal solutions,β Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. βVibe codingβ refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC β29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS β27, described as not just writing a prompt, but optimizing βthe rest of the relevant information that you would feed into the model.β Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. βI think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,β she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams βhave a tool that completes the vision for them.β While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. βThe equity issues I worry about are students who have access to paid versions of things versus free versions of things,β he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. βStudents who go to high schools or K-12 education, where theyβre taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who donβt teach that,β he said. βThen they come in, and then theyβre somehow expected to know how to use these tools.β For Genawi, those disparities begin long before students arrive on campus. βItβs very interesting to consider how lucky you were geographically,β she said. βTo be born where you were is influencing your ability to get access to these spaces.β Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. βIβm optimistic, because I think if youβre truly passionate about CS, there will definitely be a place in the field to do something you want to do,β she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta