A policy gap hiding inside a common application
A new report from admissions consulting firm Oriel Admissions, its College AI Policy Index, reviewed the publicly posted admissions policies of 50 highly selective US colleges and found a striking gap: 19 of them, including Stanford, MIT, Dartmouth, Duke, the University of Chicago, Amherst, and Williams, publish no explicit rule governing AI use in applications at all. For 18 of those schools, the only applicable language is the Common Application's general fraud clause, not a college specific AI policy.
That gap matters because most applicants to these schools submit the same personal essay across multiple colleges through the Common Application, meaning a single essay can simultaneously be governed by one college's explicit AI prohibition, another's permissive brainstorming-only rule, and a third's complete silence on the topic, all for the identical piece of writing.
The three outliers who actually drew a hard line
Only three schools in the index, Brown, Georgetown, and Harvard, explicitly prohibit AI generated content in applications outright. That is a notably small number given how central the AI-in-admissions conversation has become in higher education discourse over the past two years, and it suggests that even among institutions willing to take a public position, a flat prohibition remains the minority approach rather than the emerging norm.
The more common middle position, found at 26 of the 50 schools, permits limited AI use for brainstorming or grammar review while barring AI written text itself. That distinction, between AI as a drafting aid and AI as the actual author, is intuitive in principle but difficult to verify in practice, since no institution in the index reports using detection software to check compliance.
Why no one uses AI detection, and what that silence implies
Zero colleges in the index report using AI detection software on applications, and NYU explicitly states that it does not. That universal avoidance, even among schools with explicit written prohibitions, mirrors the broader retreat from AI detection tools happening across higher education generally, driven by well documented false positive rates and bias concerns, particularly against non-native English speakers, that have made detection tools more liability than safeguard in many institutions' assessments.
The practical effect is that even a college with a clearly stated AI prohibition is, in most cases, enforcing that rule through an honor system rather than any verification mechanism, which raises a fair question about how much the stated policy differences across these 50 schools actually translate into different real-world enforcement outcomes for applicants.
A narrower disclosure some schools are making instead
Six schools in the index, Bowdoin, Swarthmore, UNC Chapel Hill, USC, Penn, and Emory, take a different and arguably more useful approach: disclosing whether a human or an AI system reads the application itself, separate from the question of whether the applicant used AI to write it. That is a distinct transparency question, about the institution's own process rather than the applicant's conduct, and one that has received less public attention than the applicant-side AI rules.
Enterprise readers watching how institutions handle AI transparency more broadly should note this as a useful template: disclosing what AI does on your own side of a process, not just restricting what the other party can do, is a category of transparency that applies well beyond college admissions, to any organization using AI to evaluate applications, resumes, or submissions from the public.
The practical advice for applicants, and what it reveals about the policy vacuum
Oriel Admissions founder Rona Aydin's advice to applicants, follow the strictest rule any school on your list sets, is sound practical guidance, but it is also a tacit admission that the current policy landscape puts the compliance burden entirely on applicants to reconcile inconsistent or absent institutional rules themselves. A seventeen year old applying to a dozen schools with a dozen different, or entirely missing, AI policies is being asked to do policy harmonization work that the institutions themselves have not done.
That burden shifting is a familiar pattern whenever a new technology outpaces institutional policy development, and it tends to resolve only when either a dominant platform, in this case potentially the Common Application itself, sets a baseline standard, or when enough institutions converge independently on similar language that a de facto norm emerges without anyone formally coordinating it.
What this means heading into the next admissions cycle
With early application deadlines clustered around November 1, this policy gap is immediately actionable information for current applicants rather than an abstract institutional question, and counselors and families should treat checking each target school's specific AI policy, or confirming its absence, as a standard step in the application process going forward.
For higher education technology and admissions leaders, this report is a useful prompt to publish explicit AI guidance even in the absence of a detection mechanism to enforce it, since the Oriel index demonstrates that applicants and the public are now tracking which schools have taken a position and which have stayed silent, and silence is no longer a neutral choice once it is being measured and published externally.
