The Hidden Cost of AI in Higher Ed: Course Redesign at Machine Speed

April 22, 2026

Higher education has spent the last decade talking about “innovation” as if it were a discrete initiative—something you fund, pilot, assess, and then either scale or shelve. Generative AI doesn’t fit that rhythm. It is less a new tool to adopt than a new tempo imposed on everything: curriculum, assessment, student support, institutional operations, and the labor that holds those systems together.

That tension came through clearly in a recent Higher Ed Dive conversation with four campus leaders at the ASU+GSV Summit. Their comments weren’t a breathless “AI will change everything” chorus. They were, instead, a set of grounded admissions: yes, AI can help; yes, it can harm; and yes, the hard part is not buying licenses—it’s redesigning the work of teaching and learning so that human judgment stays central.

The most revealing idea in the discussion may be the least headline-friendly: course redesign is becoming a continuous process. Bret Danilowicz, president of Radford University, framed it bluntly. Faculty often revise courses on a three-to-five-year cycle; with AI, he argued, the pace compresses to yearly—or even semesterly—updates. That is not a minor scheduling tweak. It’s a structural change in workload that rubs against existing expectations about teaching, scholarship, service, and the quiet time required to produce any of them well.

This is where the conversation about AI in higher ed often gets unserious. We debate detection software, ethics statements, or which chatbot to bless, while the operational reality is that good teaching takes time. If faculty are asked to rebuild syllabi, assignments, and assessments as rapidly as AI tools and student practices evolve, institutions will need to decide what gets traded off. Do we reduce course loads? Increase instructional design support? Rebalance promotion-and-tenure expectations? Or do we pretend the old model can absorb a new rate of change—until burnout makes the decision for us?

At the same time, the leaders’ optimism wasn’t naïve. Danilowicz pointed to a familiar equity problem: students who “just” make it through with Cs often face steep employability penalties. If AI literacy becomes a baseline—covering not only prompt techniques but also ethics, limitations, and responsible use—it could raise the floor. The promise here is not that AI makes everyone brilliant. It’s that it can help more students become competently employable in workplaces that are already reshaping job roles around AI-assisted workflows.

That’s an uncomfortable but important pivot: the core question isn’t whether students will use AI. They already are. The institutional question is whether colleges will teach AI use as a set of transferable, critically informed practices—like research methods, writing, or quantitative reasoning—or leave it to informal trial-and-error (and, inevitably, unequal access to “who knows what” and “who can pay for what”).

Lisa Marsh Ryerson, president of Southern New Hampshire University, offered a second corrective to the license-first mindset. The risk, she suggested, is “giving up decision-making about learning to AI.” It’s a crisp way to name a temptation in the market: when budgets are tight and expectations are high, it’s easy to buy a platform and call it a strategy. But learning is not procurement. A credible AI plan has to begin with outcomes—who is being served, what success looks like, and where human expertise must remain in the loop—then work backward to technology choices.

Ryerson also noted something that should become routine across leadership teams: learning AI by doing. She described her executive team participating in an AI bootcamp. That detail matters because AI governance conversations often fail at the “shared understanding” stage. When leaders have no personal experience with the strengths and failure modes of these systems—hallucinations, overconfidence, hidden bias, privacy pitfalls—policy becomes either overly restrictive (AI as taboo) or overly permissive (AI as magic). Neither stance survives contact with classrooms.

Harrison Keller, president of the University of North Texas, highlighted the “two-campus” problem: AI is not just about teaching and learning. It’s also about the institution as an employer and operator—upskilling staff, changing workflows, and potentially using AI for institutional research and administrative efficiency. That expansion complicates governance. If AI is used to personalize feedback for students, what data does it draw on? If it’s used to streamline operations, how do we prevent automation from becoming a blunt instrument that erodes service quality or embeds inequities into decision-making?

Finally, UC San Diego Chancellor Pradeep Khosla framed AI as an amplifier. That metaphor is useful precisely because it is morally neutral. An amplifier increases signal. If the “signal” is thoughtful teaching, inclusive course design, and meaningful feedback, AI can help scale it. If the signal is poor pedagogy, surveillance-heavy control, or the substitution of automation for care, AI can scale that too—fast.

Put together, the leaders’ comments point to a practical agenda for the next academic year: not “adopt AI,” but rebuild the institutional conditions for responsible iteration. That means time and support for course redesign; AI literacy embedded across disciplines; leadership teams with hands-on experience; and governance that covers both learning and operations. In other words, the work is less about chasing the newest model and more about ensuring the university doesn’t lose itself while trying to keep up with the machine’s pace.

Sources

• Higher Ed Dive — “4 higher education leaders on AI’s biggest benefits and risks” (published April 21, 2026; accessed April 22, 2026)

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