Universities Are Finally Writing the AI Rulebook—And It's About Time
· Nia
For the past three years, universities have been the Wild West of AI adoption. Some professors embraced ChatGPT on day one. Others banned it outright. Students figured out how to use it regardless. And institutional leadership? Mostly silent, hoping the problem would resolve itself.
It didn't. And now, in May 2026, we're watching higher education finally confront the governance vacuum it created—with very different results depending on who's doing the governing.
SUNY's Bold Move: 64 Campuses, One Framework
Last week, the State University of New York did something remarkably ambitious: it rolled out a systemwide AI policy covering all 64 of its campuses. Not guidelines. Not suggestions. A framework with deadlines—every campus must adopt or update their own AI policy by December 31, 2026.
The policy is built on several pillars that reveal a lot about where university thinking has matured:
AI literacy in the curriculum. Not as an elective. As part of general education. Every SUNY student will be expected to evaluate and use AI tools responsibly, regardless of their major. This is the right call. AI fluency is no longer a computer science skill—it's a citizenship skill.
Data protection with teeth. SUNY's chief information security officer Jesse Sloman was explicit: "We don't want a SUNY student using a SUNY AI tool and have that data used to train external models outside of narrow, contractually defined terms." This is critical. Most universities have been handing student data to AI vendors with minimal oversight. SUNY is drawing a line.
Bias evaluation requirements. Every AI tool deployed across the system must be evaluated for discriminatory or biased outcomes. This matters enormously when you're talking about early-alert systems that flag "at-risk" students or advising tools that recommend course pathways. Without guardrails, these systems can easily encode existing inequities.
Human decision-making authority preserved. AI can inform. AI can recommend. But for high-stakes decisions affecting students—academic progress, resource access, well-being—humans remain in the loop.
Why SUNY's Approach Matters Beyond SUNY
The 64-campus system serves nearly 400,000 students. When a system that large implements a coherent AI framework, it becomes a de facto template for other institutions. Smaller schools that lack the resources to develop AI governance from scratch will look at SUNY's model and adapt it.
The timing also matters. This comes alongside state-level AI governance efforts from Governor Hochul's office, meaning SUNY's framework isn't operating in a regulatory vacuum—it's aligning with broader policy direction. That coordination between institutional and state governance is rare and valuable.
SUNY chancellor John B. King Jr. acknowledged the stakes: "AI usage is in its infancy across much of higher education and government." He's right, and that's exactly why getting the foundation right matters so much. The policies written today will shape how millions of students experience AI-augmented learning for the next decade.
ASU's Cautionary Tale: When Innovation Outpaces Governance
Contrast SUNY's deliberate, consultation-heavy approach with what's happening at Arizona State University—and you'll see exactly why governance matters.
ASU soft-launched a product called "Atomic" that uses AI to build personalized learning modules. For $5 a month, anyone can prompt a chatbot named Atom to create a custom course pulling from ASU professors' instructional materials—video lectures, slide decks, assignments—all repackaged into bite-sized learning experiences.
There's just one problem: nobody told the professors.
Literature professor Chris Hanlon discovered his own face staring back at him from an Atom-generated module he'd never consented to. His video lectures had been clipped, decontextualized, and embedded alongside what appeared to be AI-generated material that attempted—and failed—to provide context for his content. The AI called literary theorist Cleanth Brooks "Client Brooks" throughout.
Multiple professors confirmed they'd never heard of Atomic, were never consulted about their materials being used, and had no ability to opt out.
This is what happens when "move fast and break things" meets academic intellectual property, pedagogical integrity, and faculty governance. ASU president Michael Crow, when questioned about it at a faculty Q&A, seemed surprised the tool existed in its current form—acknowledging it "was not really ready for prime time."
The Intellectual Property Minefield
The ASU situation exposes a fundamental tension that most universities haven't resolved: who owns instructional materials?
Under ASU's Board of Regents policy, the board claims ownership of "any intellectual property created by a university or board employee in the course and scope of employment." That includes the lectures, slides, and videos professors upload to Canvas (their learning management system).
But "owning" content and "repurposing it without notice through an AI system that strips context and introduces errors" are very different things. Legal ownership doesn't resolve the ethical and pedagogical concerns:
- What happens when AI misrepresents a professor's expertise?
- Who's liable when a student learns something incorrect from a "personalized" module?
- How do professors maintain academic standards when their content is algorithmically remixed?
Religious studies professor Michael Ostling put it bluntly: "I have content on my Canvas shelves that would be very inappropriate to show up without context in a course. Not only do I think the students will be poorly served because they might learn things that aren't true, but it could potentially get me in trouble."
The Bigger Picture: AI Is Splitting Higher Ed in Two
What we're witnessing in 2026 is a fundamental divergence in how institutions approach AI:
The Governance-First Camp (SUNY, and increasingly systems like University of California and Big Ten schools): AI adoption should be deliberate, consultative, and rights-respecting. Build the framework first, then scale. Involve faculty governance. Prioritize student data protection and equity.
The Innovation-First Camp (ASU, some for-profit institutions, and ed-tech startups): Speed matters more than consensus. Ship it, learn from it, fix it later. Faculty will adapt. Students want personalization now.
Neither approach is entirely wrong. The governance-first camp risks moving so slowly that students graduate without ever experiencing AI-augmented learning. The innovation-first camp risks building systems that harm the people they're supposed to serve.
But I'll take SUNY's approach every time. Here's why.
Why Deliberation Wins in Education
Education isn't a consumer product. A bad AI recommendation on Netflix wastes 90 minutes of your evening. A bad AI recommendation in an advising system can derail a student's academic trajectory. A biased early-alert algorithm can disproportionately flag students of color as "at-risk" and trigger interventions that feel more like surveillance than support.
The 20 "AI for the Public Good Fellows" that SUNY is deploying—faculty and staff who will work across disciplines to integrate AI responsibly—represent exactly the kind of investment needed. Not top-down mandates from administrators who've never taught a class. Not vendor-driven implementations optimized for licensing revenue. Faculty-led integration that respects pedagogical expertise.
The Empire AI consortium and the new AI research center at SUNY Binghamton add another dimension: students aren't just consuming AI tools, they're building and studying them. That's the difference between preparing students to use today's AI and preparing them to shape tomorrow's.
What Every University Should Be Doing Right Now
Based on what's working (and what's catastrophically failing) across higher education:
The Stakes
Higher education is under unprecedented pressure right now. College closures are accelerating—nearly 1,000 employees lost their jobs in April alone as institutions like Hampshire College and Anna Maria College shut down. Federal funding faces new political conditions. States are reviewing curricula.
In this environment, AI isn't optional. It's a survival tool for institutions trying to serve more students with fewer resources. But that urgency makes governance more important, not less. When you're under pressure to cut costs, it's tempting to deploy AI systems without adequate oversight. That's exactly when harm is most likely.
SUNY's policy isn't perfect. No first-generation framework is. But it represents something important: the recognition that AI in education isn't just a technology question. It's a question about equity, about student agency, about what learning means when machines can generate plausible knowledge on demand.
The universities that get this right will thrive. The ones that move fast and break things will discover that in education, the things you break are people's futures.
And those don't reassemble easily.