Insights from Dr. Evan Kropp, Executive Director of Distance Education and Affiliate Faculty, University of Florida College of Journalism and Communications. This article draws on his analysis originally published in University Business.
Twenty-five years ago, higher education leaders were sounding the alarm about a transformation already underway: the internet was reshaping the workplace, and colleges and universities needed to catch up. The advice, in hindsight, sounds almost quaint — a 2001 article described how every employee suddenly had “this little box called a computer” on their desk, connected to both an intranet and the internet.
The call was earnest, and at the time, justified. Higher education has long been slow to adapt to technological change. According to Dr. Evan Kropp, Executive Director of Distance Education and affiliate faculty at the University of Florida College of Journalism and Communications, that same lag is now unfolding around artificial intelligence — and this time, the cost of moving slowly may be far higher.
“The window of time for institutions to take action is narrowing,” Kropp warns. “If they fail to adapt, the risk is not falling behind. The risk is becoming obsolete in a rapidly transforming world.”
Why the AI Moment Isn’t Like the Internet Moment
Kropp acknowledges the historical parallel between the internet revolution and the rise of AI — but he’s careful to point out where the two diverge. And the difference, he argues, has profound implications for how colleges and universities should respond.
“AI is moving much faster,” he writes. The internet was a transformative shift, but the core skills it required — searching, navigating, communicating online — have remained relatively stable for two decades. A student trained in basic internet competencies in 2001 was, in many ways, still well-prepared for the workforce of 2021.
AI offers no such stability. The capabilities, tools, and applications are evolving on a monthly basis. “The AI-related skills that employers want and need today won’t be the same ones that they want and need tomorrow,” Kropp writes. That instability changes what it means to prepare students for an AI-driven workforce. Employability today is no guarantee of employability in two or three years — and any approach to AI education that focuses only on the present moment will produce graduates who are competent at launch and adrift shortly thereafter.
“Employability in the AI-driven workforce therefore does not mean durability,” Kropp argues, “and we mustn’t neglect the latter.”
Teach Principles, Not Platforms
Kropp’s central recommendation flows directly from that observation. Rather than building curricula around specific tools — many of which will be obsolete by the time students graduate — institutions should focus on broad, durable skills and principles that travel well across changing technologies.
He points to critical thinking applied specifically to AI use, decision-making under uncertainty, and preventing cognitive diminishment or offloading as examples of the kinds of capabilities that hold their value regardless of which AI platform dominates next year.
To illustrate, Kropp draws on his own teaching experience. In a social media marketing course at the University of Florida, he and his colleagues deliberately avoided centering instruction on individual platforms like Instagram or Twitter/X. Instead, they emphasized strategic communication, audience understanding, and data-driven decision-making.
“Platforms change and become obsolete,” he writes, “but through it all the broad skills remain key.”
The same logic, he argues, applies tenfold to AI. The specific tools students master in their first year of college may not exist by their senior year — but the judgment, frameworks, and ethical reasoning they build around AI will remain relevant for decades.
AI Belongs Across the Curriculum, Not Beside It
One of the temptations Kropp explicitly warns against is treating AI as something that can be siloed into a standalone course or a single graduation requirement. He returns to the internet analogy to make the point.
“Just as no one could imagine confining learning the internet to a single college course today,” he writes, “there will soon come a time when AI will be thought of the same way.”
The implication is significant. AI literacy needs to be embedded across the curriculum, with discipline-specific applications taught wherever students are studying. Medicine students need to understand how AI is reshaping diagnostics. Law students need to understand AI in legal research. Architecture students need to engage with design computation. The list extends into virtually every field.
A curriculum-wide approach isn’t an aspirational ideal in Kropp’s framing. It’s the minimum bar for institutions that want to remain credible to students entering the workforce.
The Faculty Readiness Gap
There’s a problem with this vision, however, and Kropp doesn’t avoid it. Institutions cannot prepare students for AI if the people teaching them aren’t themselves engaged with AI — and the data suggests a meaningful gap.
He cites a Chronicle of Higher Education report finding that administrators are far more likely than faculty to see preparing students for AI as part of the faculty role. Some faculty members remain wary of the technology, hesitant to adopt it, or uncertain about how to incorporate it into their teaching.
The barrier isn’t only attitudinal. According to EDUCAUSE data Kropp references, 56% of faculty using AI tools say those tools are not provided by their institutions — pointing to a meaningful institutional support gap. Even faculty members who are eager to engage with AI are often doing so without the resources or infrastructure their institutions should be providing.
The conclusion Kropp draws is direct. Faculty enthusiasm and institutional investment have to move together. Neither alone will produce graduates ready for an AI-driven workforce.
What Real Readiness Looks Like
Kropp’s closing argument reframes what higher education should mean by “job readiness” in the AI era. It is not, he insists, about adding a course, mastering a specific tool, or satisfying a checkbox requirement.
“It means re-envisioning what we mean by job readiness itself,” he writes, “and focusing less on just immediate employability and more on long-term durability.” It means integrating AI across the entire curriculum and adapting its applications to specific disciplines. And it means faculty stepping up to learn, use, and model productive, ethical engagement with AI.
The stakes, in Kropp’s framing, are existential for higher education itself. Institutions that move now have a chance to lead. Those that delay risk producing graduates whose credentials feel outdated the day they’re handed out — and risk becoming irrelevant in a workforce that is already moving on.
Dr. Evan Kropp is the Executive Director of Distance Education and an affiliate faculty member at the University of Florida College of Journalism and Communications. His work focuses on digital learning, strategic communication, and the integration of emerging technologies into higher education.