On November 12, the Department of Education signaled where higher ed is headed next: the FY 2025 FIPSE Special Projects competition (due December 3) names artificial intelligence as a national priority for postsecondary innovation. Federal dollars are now actively accelerating AI’s spread across classrooms, advising, and campus operations. The question is no longer whether AI will be used—it already is—but whether institutions can prove they’re using it responsibly, with oversight, bias safeguards, and data protections that can withstand accreditation review and federal grant monitoring. Seen through that lens, the real divide going into 2026 isn’t pro-AI versus anti-AI; it’s governed adoption versus unmanaged adoption.
In October 2025, the Council of Regional Accrediting Commissions released a field-level statement on AI in learning evaluation and credit recognition. C-RAC represents the seven regional institutional accreditors responsible for roughly 3,000 U.S. colleges and universities.C-RAC allows AI in learning evaluation as long as colleges can prove three things: transparency, human accountability, and bias protection. Without that proof, AI use becomes an accreditation risk.
In July 2025, the U.S. Department of Education issued a Dear Colleague Letter saying colleges may use existing federal grant funds for AI in instruction, student support, and workforce programs if they comply with civil-rights and privacy law. The letter also makes the expectations clear: Humans must stay responsible for high-stakes decisions, AI must be checked for discrimination, student data must be protected, and results must be monitored over time.
Read together, these statements create a new operating reality. AI is now both fundable and reviewable. Institutions that scale adoption without governance are no longer merely taking reputational risk; they are building vulnerabilities likely to surface during accreditation reviews or federal grant monitoring.
You don’t have to imagine what happens when AI adoption outruns governance. In November 2025, students in a government-funded coding apprenticeship at the University of Staffordshire reported that much of their course relied on AI-generated slides and synthetic voiceovers, with limited human instruction. Learners said the content was inconsistent and that they felt misled about what they were paying for. The university pointed to its ethical AI framework in response, but the episode exposed the new compliance reality: A framework is only defensible if the lived learning experience, oversight, and transparency match what the policy promises.
Contrast that with institutions treating governance as infrastructure. Northeastern University partnered with Anthropic in 2025 to pilot Claude for Education across its global university system, giving students, faculty, and staff secure access to the tool. The university paired the rollout with an internal AI working group and a stated emphasis on using Claude to support learning rather than substitute for it. That combination—enterprise deployment plus formal oversight and learning-value intent—is what makes the pilot credible in the DOE-and-accreditor era.
Another instructive case is the University of Pittsburgh. In 2024, Pitt convened a semester-long, faculty-led, interdisciplinary governance process to develop campuswide recommendations for responsible generative-AI use in research and education. The effort used recurring working-group sessions, three focus groups, and a survey to gather perspectives across academic and administrative contexts, producing a shared set of “points to consider” for AI adoption. By treating AI policy as a shared-governance design problem rather than a one-off memo, Pitt created the kind of documented, iterative oversight accreditors recognize as legitimate quality assurance.
So what counts as “real governance” under accreditation and DOE expectations? CHEA’s Guiding Principles for AI in Accreditation and Recognition, released in February 2025, provide a clean answer. CHEA does not accredit institutions directly, but because it recognizes accreditors, its principles shape what peer reviewers increasingly look for. The guidance centers on human accountability, transparency, reliability, equity, privacy/security, and mission alignment. In practice, that translates into an evidence trail showing who owns AI decisions, which uses are permitted or prohibited, how risk is assessed, how people are trained, how impacts are monitored, and how policies evolve over time.
WCET’s revised 2025 AI Education Policy and Practice Ecosystem Framework makes the same point through an implementation lens. The framework was updated precisely because higher ed needed to move toward a prioritized roadmap: Governance ownership and risk management first, then policy integration, training, monitoring, and only then broad scaling. WCET explicitly warns that the framework is not one-size-fits-all, but it is clear about sequencing. If foundational safeguards are weak, scaling is fragile by design.
The simplest way to understand the moment is this: Accreditors and federal funders are not asking colleges to slow AI adoption. They are asking colleges to make adoption provable. Leaders do not need a perfect, future-proof policy to start. They need an operating system that can show, at any point in time, that AI is being used with human oversight, equity protections, data safeguards, and continuous evaluation. Institutions that cannot produce those artifacts on demand are not governance-ready, no matter how optimistic their public messaging sounds.
That creates a very near-term agenda. Presidents, provosts, CIOs, and boards should treat the next 90 days as a governance build window. The campuses that formalize authority, establish risk tiering, integrate AI into existing policy stacks, require role-based training, monitor tools for bias and drift, and maintain a living evidence record will be able to adopt AI confidently and draw on federal dollars without fear. The campuses that delay will either freeze in uncertainty—or sprint into adoption and hope no one asks for documentation later.
AI is no longer a side experiment in higher education. It is becoming part of the institutional quality-assurance and compliance fabric. The era of AI policy talk is ending. The era of AI governance evidence has begun.