AI fluency is becoming the new general education

Faith Maeba is 21 and a psychology senior at Virginia Commonwealth University. This fall she is adding an AI minor because, as she puts it, every graduate program she is applying to has started asking how machine learning is changing the way people behave at work. Her mother suggested it. Her department did not require it.

At Purdue, the requirement is now university-wide. The school mandates an AI course for every undergraduate, regardless of major. Harvard teaches large language models, copyright, and disinformation in its freshman writing program. Ohio State runs required AI fluency workshops with hands-on lab time. VCU is standing up AI minors for students who do not study computer science. The pattern on each campus is the same: AI is leaving the computer science building and landing in the general education core.

The push sits where the labor market has cooled most. Entry-level software hiring has slowed as AI agents take on more of the work that new graduates used to do. According to a Stanford economist writing in Business Insider, US computer science undergraduate enrollment fell for the first time in roughly two decades after the release of ChatGPT in late 2022. The same period is when employers across fields, from consulting to marketing to clinical psychology, began listing AI fluency on job postings and graduate program applications.

The response runs the other way at the institutional level. UT Austin computer science chair Peter Stone developed an introductory AI essentials course for non-CS majors and argues, in comments to the Tribune Chronicle's wire report on the AI boom in higher education, that the subject belongs alongside math, reading, and writing as a general education baseline. Stone's department built the original AI pipeline on campus. He is now teaching outside it.

Northwestern computer science chair Samir Khuller is running the same experiment from the other side of the budget. CS major headcount at his school has shrunk after a prior doubling, and the department now teaches more non-majors than it did before ChatGPT, according to the same wire report. The course load has not dropped. The composition of the room has changed.

The broader enrollment picture is mixed but informative. National Student Clearinghouse Research Center data tracks the spring and fall shifts across US higher education, and Higher Ed Dive reported that overall US undergraduate headcounts ticked up about 1% this spring, with graduate headcounts taking a hit. Industry analysis from Encoura describes the CS pipeline as moving from a surge to a shift: a softening of demand at the top of the funnel even as institutions try to push AI exposure into every other major.

That is the inversion. The first universities to ride the AI wave were the computer science departments, and they grew accordingly. The universities now requiring AI fluency for every student are responding to a different signal. The work graduates will do is being reshaped before they arrive, and the institutions that credential them want some documented exposure to count for it, even if it is a single required course.

The shift has real costs. CS departments are absorbing teaching load from non-majors while their own major pipeline shrinks. AI literacy coursework varies in rigor, from a one-credit workshop to a full minor. Fluency is not a guaranteed labor hedge: an employer asking about AI on a job posting is not the same as an employer paying for it. Treating AI exposure as a baseline, like math, sets a floor. It does not promise a ceiling.

For Maeba, the question is more immediate. She graduates in May with a psychology degree and an AI minor. The minor cost her two electives and a statistics prerequisite. She is betting the two together, not the psychology alone, will be what graduate programs read.