The Disruption Confidence Cycle in Higher Education

By Joel Comm | A Trusted Voice in a Noisy Tech World

Every professor who has watched students write entire papers with ChatGPT knows higher education’s disruption has already begun.

I’ve had a front-row seat to every major technology revolution since 1980. Personal computers. The internet. Social media. Mobile. The cloud. Now AI. Here’s what 44+ years on the front lines has taught me: every disruption follows the same predictable pattern.

Higher education is deep in the Doubt phase right now. The good news?

What comes next is predictable too.

Why Higher Education is Drowning in Doubt

Picture the provost who just sat through another AI policy meeting. Half her faculty thinks AI will destroy academic integrity. The other half thinks the institution will be left behind if it doesn’t move now. She’s paralyzed.

That scene is playing out at thousands of institutions right now. And it’s textbook Doubt phase behavior — the second stage of the Disruption Confidence Cycle™.

Higher education is showing every classic symptom:

Analysis paralysis. Committees forming sub-committees to study AI policies that other committees will review next semester.

Fear-based decision making. Banning ChatGPT outright instead of teaching students how to use it responsibly.

Conflicting expert opinions. One consultant says AI will replace professors. Another says it’s a fancy calculator. Both get paid.

I watched the same scene unfold in 1995 when enterprises tried to figure out what to do about something called “the World Wide Web.” Should we ignore it?

Control it?

How do we protect the existing business model?

Most of the institutions asking those questions are no longer asking any questions at all.

The universities stuck in Doubt today are asking the wrong questions entirely.

The Disruption Confidence Cycle™ — All Five Stages

Here is the full arc every industry travels through. Higher education is no exception.

1. Disruption. A new technology arrives and forces a real change in how the industry works. The status quo stops being reliable. For higher ed, this happened the moment generative AI became free, fast, and good enough to write a passable freshman essay. The old assumptions about take-home assignments, plagiarism detection, and information transfer broke overnight.

2. Doubt. Leaders question whether the change is real, whether their team can handle it, and whether to act now or wait. Anxiety and paralysis dominate. This is where most colleges and universities sit today. Faculty senates debate. Policies stall. Pilots get proposed and shelved.

3. Clarity. The fog lifts. Leaders see which use cases matter, which to ignore, and which questions to ask. In higher ed, Clarity looks like this: assessment redesigned around synthesis instead of recall. Composition courses that teach students to direct, critique, and edit AI output. Tutoring centers using AI to extend reach without replacing humans. Faculty stop asking “should we allow this?” and start asking “what does mastery look like now?”

4. Confidence. Teams begin to operate with the new technology as a regular tool. Skepticism gets replaced by competence and demonstrated results. Departments run AI-integrated courses and can point to specific student outcomes. Faculty who were terrified eighteen months ago are now leading workshops for their peers.

5. Momentum. The institution compounds early wins into durable advantage. AI stops being a “project” and becomes invisible infrastructure — the way we no longer talk about “using the internet.” Recruiting, advising, research, instruction, and operations all benefit. The institutions that get here first will be the ones writing the case studies. The ones that don’t will be the case studies.

The only real variable is how fast you move through the cycle.

One Pattern, Six Revolutions

The companies that thrived through the internet revolution weren’t necessarily the ones with the best technology. They were the ones that moved through the cycle the fastest. Same with social media. Same with mobile. Same with cloud.

The winners weren’t the ones with perfect strategies on day one. They were the ones who got comfortable being uncomfortable.

Higher education has a unique twist. Your “customers” — students — are already using AI daily. Your “product” — education — is built on assumptions that AI is actively challenging.

When a student can generate a passable essay in 30 seconds, what’s the point of the essay assignment?

When AI can solve complex problems instantly, how do you teach problem-solving?

When information is free and instant, what is a lecture for?

These aren’t rhetorical questions. They’re the questions that separate Doubt-stage institutions from Confidence-stage ones.

The Questions Your Team Should Be Asking Right Now

Stop asking “Should we use AI?” Start asking these:

“What are our students actually doing with AI?” Not what you think. What they’re really doing. Survey them. The gap between faculty assumptions and student behavior is the first thing that will shock you.

“Which of our current teaching methods become more valuable with AI, and which become obsolete?” Memorization: less valuable. Critical thinking: more valuable. Synthesis, evaluation, original judgment: essential.

“How can AI help us deliver on our core mission more effectively?” If the mission is student success, how might AI accelerate it?

If it’s research, how might AI amplify your faculty’s capabilities?

“What would we do differently if we were founding this institution today?” This question cuts through decades of accumulated assumptions faster than any strategic plan.

The institutions asking these questions today will be the ones leading the conversation tomorrow.

Moving From Doubt to Clarity to Confidence

Here’s how you actually move through the cycle: start small, measure everything, scale what works.

Don’t try to solve AI across the entire institution. Pick one department. Computer science is the obvious starter, but composition, business, and nursing have all proven to be strong launchpads at institutions experimenting publicly today.

Integrate AI into a specific course or workflow. Not to replace learning, but to redirect student effort toward higher-order skills. In programming, that means less time on syntax and more on architecture.

In writing, less time on first drafts and more on argument and revision. In advising, less time on transcript review and more on actual human conversation.

Measure outcomes. Share the wins. Then expand.

That’s how confidence builds. One success at a time.

A few principles for leaders:

Create psychological safety for experimentation. The faculty member who tries an AI tutoring pilot and fails should be celebrated, not criticized. Failure is data.

Focus on augmentation, not replacement. AI won’t replace professors who understand how to use AI. But professors who understand AI will out-teach those who don’t.

Measure student outcomes, not technology adoption. The goal isn’t “we use AI.” It’s “our students succeed because of how we use AI.”

The Real Opportunity

Here’s what most higher education leaders miss: AI isn’t just another technology to integrate. It’s an opportunity to reimagine education itself.

What if lectures weren’t about information transfer anymore, but about inspiration and connection?

AI can handle information transfer.

What if homework wasn’t about demonstrating knowledge acquisition, but about applying knowledge to novel problems?

AI makes knowledge acquisition trivial.

What if assessment wasn’t about what students can remember, but about what they can create, synthesize, and defend?

AI changes everything about how we should think about assessment.

The universities that embrace this level of reimagining won’t just survive the AI revolution. They’ll lead it.

That requires moving through the Disruption Confidence Cycle faster than your competitors. It requires treating this moment not as a threat to manage, but as an opportunity to seize.

Students are already using AI. Employers already expect AI literacy. Other institutions are already experimenting.

The question isn’t whether AI will transform higher education. It’s whether you’ll lead that transformation or become a footnote in it.

Your Next Move This Week

Don’t wait for the next strategic planning cycle. Do these three things in the next seven days:

1.

Talk to five students. Ask exactly how they’re using AI in your courses right now. Don’t judge. Just listen. 2.

Identify one AI-curious faculty member and give them permission, a small budget, and air cover to run a single-course pilot next term. 3.

Pick one assessment in your own department and redesign it assuming every student has AI assistance. See what changes.

Three small moves. One week. That’s how you stop drowning in Doubt and start building Clarity.

And if you’re a provost, president, or conference organizer trying to help your faculty and peers navigate this moment, I’m available to speak at higher education events, leadership retreats, and system-wide convenings on the Disruption Confidence Cycle™ and what it means for your institution.

Bring me in before the next academic year — because the institutions that get to Momentum first won’t be waiting for the rest to catch up.