A new argument making the rounds says the best use of artificial intelligence in higher education isn’t grading papers or writing essays for students — it’s replacing the people who decide who gets in.
The case comes from Fox News contributor Hugh Hewitt, who points to a April 2023 study by the College and University Professional Association for Human Resources covering 12,042 admissions employees at 940 institutions — an average of more than a dozen staffers per school. With more than 4,000 degree-granting institutions in the U.S., Hewitt estimates the total admissions workforce at roughly 40,000 people, and argues nearly all of that work could be done better, faster, and more fairly by AI.
The logic is straightforward. College admissions runs on paper and numbers — test scores, GPAs, essays, resumes, recommendation letters — submitted by tens of thousands of applicants who all want the same thing: a fair shot. That kind of large-scale, data-driven sorting is precisely what AI systems already do well. Test scores and GPAs can be compiled and ranked in minutes. Essays can be scanned for originality and for signs of outside help. Recommendation letters can be checked for authenticity and substance.
Merit factors AI could weigh — and the ones it can’t
Hewitt argues AI models can be built to give legitimate weight to things like in-state or out-of-state status, gender, family income, difficult life circumstances, geographic and economic diversity, the rigor of an applicant’s high school, athletic ability, legacy status, musical or theater talent, foreign-language fluency, and whether an applicant is a first-generation college student or has worked while in school — all factors tied to long-term student success.
What AI models can be instructed to ignore entirely, he says, is race, ethnicity, and religion — categories whose use in admissions decisions is now restricted under federal law and Supreme Court precedent.
That restriction is the heart of the argument. College admissions has spent decades under suspicion of politicization, with critics accusing admissions offices of quietly weighing race and other prohibited factors despite the law. Hewitt’s pitch is that an AI-driven process, with its weighting criteria laid out for anyone to inspect, would give schools a transparent, auditable defense against discrimination lawsuits — and give applicants a process that isn’t shaped by the personal biases of whoever happens to be reading their file.
