Think about the last AI conversation you were part of on your campus.
Was it about a dazzling new tool someone wanted to get up and running immediately? A policy that got bogged down? A student question that made you realize you don’t have the answer?
We’re all having those conversations each day, sometimes every day… and sometimes it’s even the same conversation but with different groups, different units! And nothing ever seems to get resolved.
Higher education is not short on AI activity. People are experimenting everywhere. Students are using AI tools to study, draft, summarize, search, and make sense of academic expectations. Faculty are rethinking assignments, feedback, trying to crack the code on AI literacy (or is it fluency?). Staff and administrators are exploring AI-supported workflows in advising, student support, communications, analytics, curriculum, and institutional operations.
It’s difficult work. But it all leads to a harder, more urgent question: since those conversations are happening everywhere all at once, how are you capturing what’s shared? How are you connecting the experiments?
What is your institution actually learning?
Activity Is Not Strategy
It’s easy to mistake movement for progress. A new tool appears. A department tries it. A task force forms. A workshop is scheduled. A policy is drafted. A few early adopters share examples. A few skeptics raise important concerns. Someone starts collecting use cases. Someone else asks whether we are falling behind.
All that activity may be necessary. But activity alone is not strategy.
Strategy requires a way to connect action to learning. Without that connection, AI experimentation remains scattered. One instructor learns something valuable about student reflection, but the insight stays inside one course. A pilot surfaces workflow friction, but the lesson never reaches the people who can act on it.
We get trapped in a cycle of action without reflection, momentum without real progress. We experiment constantly without building the capacity to learn from it all.
The Wrong Game
For the last few years, higher ed has been stuck in a sprint. AI tools change overnight. Students were using and misusing them before faculty knew there was a roll out. Vendors make utopian promises. And so, the work becomes a scramble: answer the question, draft the guidance, schedule the workshop, launch the pilot, revise it all again next month.
But higher education cannot win by chasing the pace of this technology. We will always be behind the next flashy product release, the incendiary headline, the rolling waves of anxiety.
The better move – maybe the only move – is to change the game.
Instead of asking, “How do we keep up with AI?” maybe we need to do what higher education is built to do: ask better questions, test ideas carefully, make meaning from evidence, and share what we learn.
That sounds simple, but it changes the work. It moves us away from the chaos of reactivity and toward institutional learning. It invites us to treat AI experimentation at any level as a way to generate insight, improve practice, clarify values, and make better decisions under uncertainty.
An Invitation to Slow Down Productively
At OLC Accelerate 2026, I’ll be facilitating a three-hour Immersion Session, “Working in Uncertainty: Learning from AI Experimentation in Online Education,” designed around this exact challenge.
The session is not an attempt to cover every AI issue facing online education. Instead, it focuses on one core capacity: how to design learning loops that help people make better decisions. Participants will begin by taking stock of their own context. Where is AI experimentation already happening? How are people currently learning from it? How are those lessons being turned into action?
From there, each participant or team can select one real AI-related challenge or opportunity from their own teaching, design, leadership, or operational context. They will use that example to build a practical learning loop: a clear learning question, the signals worth observing, the team you need to make sense of it all, possible actions that might follow.
Along the way, we’ll share some implementation stories, not because I’ve got all the right answers. But because the more we share examples, the more we connect over stories of how experimentation can bottom out, fall flat, and explode in our faces, the more we set ourselves up to pair insight with intentional reflection.
The goal is for participants to leave with something usable: not a generic AI strategy, not a seven-part template filled out at the surface, but a draft learning loop for a real challenge they can take back to their own institutions.
The real work for higher education at this moment is not just adopting AI. It might not even be about AI at all. But if we lean into the habits, structures, and conversations that help us learn from all this reveals we can design better ways to learn.
Nathan Pritts currently serves as Principal AI Strategist and Professor at the University of Arizona Global Campus where he leads cross-functional work spanning academic innovation, operational workflows, student success, and organizational change. Grounded in large-scale online learning environments, his work focuses on helping institutions move from experimentation to sustainable, human-centered practice. Prior to his AI strategy role, Pritts served as Program Chair of First-Year Writing, leading one of the university’s largest academic units and overseeing curriculum, assessment, faculty development, and instructional quality across high-enrollment general education courses. He is the editor of Empowering Educational Development and Faculty Growth With AI and the author or co-author of fourteen books, including Film: From Watching to Seeing, Essentials of Academic Writing, and the forthcoming AI as a Creative Partner: An Introduction from Routledge.
