9% Used AI to Cheat. The Other 91% Are Waiting on Their Faculty.
What the Science study of 95,513 students actually tells institutions to fix — and the three faculty-investment moves separating the systems getting it right from the ones writing checks
On May 21, Science published the largest study of its kind on how college students actually use generative AI. Igor Chirikov of UC Berkeley’s Center for Studies in Higher Education, Ivan Smirnov of the University of Technology Sydney, and René Kizilcec of Cornell analyzed survey data from 95,513 students across a representative sample of 20 major public research universities. The headline number that traveled: 9 percent of students used generative AI to cheat.
The number that did not travel: 91 percent did not.
Most coverage ran the story as a cheating story. Read the study itself and it is something closer to the opposite: a faculty-capacity story wearing an academic-integrity headline. Roughly two-thirds of students reported using generative AI during the study period. Thirty-seven percent use it regularly — monthly or more. Regular use runs highest in computer science (62 percent), mathematics (53 percent), and business (51 percent). The tools are already inside the coursework of nearly every discipline that leads to employment. The institutional question is not how to catch the 9 percent. It is what the other 91 percent need from their faculty in the next twelve months — and whether faculty are getting the time, training, and craft to deliver it.
Take the 9 Percent Seriously — Including the Objection
The first objection to the 9 percent figure is that it is self-reported and therefore deflated: students under-admit cheating, and research on AI-assisted work shows students routinely misjudge how much of the output was theirs. The objection deserves a straight answer, and the study’s design partly supplies one. The researchers did not simply ask students whether they cheated. They used an indirect-questioning method built specifically to estimate sensitive behaviors that direct survey questions undercount. The 9 percent is a more defensible number than the usual self-report.
But grant the objection entirely. Suppose the true figure is double, or triple. The institutional conclusion does not move. The study found misuse rises to 26 percent among daily users — the more embedded the tool, the more the line blurs — and that use and misuse vary so much by discipline that the authors’ own prescription is discipline-specific assessment reform, not blanket bans and not universal detection regimes. “Assessment reform is necessary and urgent,” Kizilcec said when the study released. Whatever the real cheating number is, the answer runs through the people who design assessments. That is faculty. And almost no institution is resourcing them to do it.
The Support Gap, Quantified
The Digital Education Council’s Global AI Faculty Survey — 1,681 faculty across 52 institutions in 28 countries — found that just 6 percent of faculty fully agree their institution has provided sufficient resources to build their AI literacy. Only 17 percent rate themselves at an advanced level with the technology they are now expected to design assessment around; 40 percent describe themselves as just beginning. The Tyton Partners Time for Class study, drawing on more than 3,000 instructors and administrators, adds the structural version of the same finding: most institutions still have no generative AI policy at all, and instructors report that monitoring student AI use is adding work to their load, not removing it.
Put the two datasets side by side and the diagnosis writes itself. Student use: roughly two-thirds and climbing. Faculty support: single digits, by faculty’s own account. The assessment-validity problem the Science study surfaced is not first a student problem. It is a faculty-support problem. The 9 percent who cheat are the symptom. The architecture that makes cheating the easier path than learning is the cause — and faculty are the only people who can rebuild that architecture.
Same Year, Same Use Case, Opposite Design: SUNY and CSU
Two public systems made faculty-AI decisions this spring. The design contrast is the most instructive thing happening in higher ed right now.
SUNY set a systemwide AI framework, reported by Inside Higher Ed on May 4, that runs the integration through faculty by design. A cohort of twenty AI for the Public Good Fellows — faculty and staff across disciplines, in compensated fellowship roles — consults with colleagues, instructional designers, and librarians to build AI into coursework across the system’s 64 campuses. AI literacy is embedded in the Information Literacy core competency of SUNY’s general education framework, so the integration work has a curricular destination rather than floating as professional development. Every campus must adopt or update its own AI guidelines by December 31. Faculty are the integration layer, the fellows are the support structure, and the deadline forces the governance work.
The California State University system renewed its OpenAI contract in May at $13 million a year for three years — $39 million to put ChatGPT Edu in front of more than 470,000 students and 63,000 faculty and staff — while the system faces $144 million in budget cuts and while its own survey of more than 94,000 students, faculty, and staff found 59 percent of faculty skeptical that AI is benefiting education at all. Faculty delivered a petition opposing renewal; NPR reported that internal planning documents had described the partnership as a branding opportunity. Whatever else the renewal is, it is a sequencing decision: tool access first, faculty enablement second.
Same use case. Same year. Opposite design choice. One system bought capacity in its people. The other bought seats. The Science study is effectively a referendum on which sequence holds up — because assessment reform cannot be procured. It has to be built, by faculty, on paid time.
Below, for paid subscribers: the three moves the institutions getting this right are actually making — with the operational detail to bring to your own provost’s council — plus the governance templates archive: the procurement-gate language, AI review-body charters, and FERPA-aligned data-tier definitions your campus can adapt before its own December deadline arrives.