The Communication Challenge at the Heart of AI Adoption in Higher Education

by Bruce W. Fraser, Executive Director of AI Initiatives, Indian River State College

Recently, I had a conversation about AI adoption with a colleague from another college in Florida. This person is responsible for AI training at her institution, and she is one of the brightest, most thoughtful leaders I’ve encountered in the AI space. We were discussing AI agents and the new generation of AI tools arriving on our campuses.

She told me her faculty wants nothing to do with one of the major platforms I mentioned. The view at her institution, she said, is that the company is “creating tools to help students cheat.”

This isn’t a perspective I’d heard directly from our faculty, but I admit I’m probably not the person that a skeptical professor would talk to about such misgivings. That said, I suspect this is a fairly common view among teaching faculty.

An Alternative Philosophy

At Indian River, we’ve framed AI as a force multiplier of human work. This is not naive. We, too, are aware that AI can be used in ways that diminish intellectual capacity and allow students to cheat.

What drives our philosophy is the belief that tectonic shifts in technology inevitably alter how humans understand knowledge, cognition, and education. This lesson is derived from other historical shifts, such as the creation of the written word and the invention of the printing press. As Walter Ong documents in his book Orality and Literacy, changes in information technology initiate changes in epistemology and, as a result, in social institutions such as education.

We believe that resistance to AI stems from longstanding educational practices designed around literacy and print rather than dynamic intelligence. In a world in which students labor to create an essay, the final product stands as a useful (if imperfect) proxy for learning. Text is static, meaning the student must make an effort to extract its meaning, organize it, and make intelligent claims about it. This is why the essay can be seen as evidence of learning.

In the age of generative AI (and given the biologically rooted human propensity to take the path of least resistance), this approach to assessment no longer works. ChatGPT, Claude, and new agentic tools can do the work that we’ve taken to be essential to learning. In this context, it seems obvious that AI is a threat to learning in general.

It isn’t (not intrinsically, at least). Replacing text with dynamic intelligence as the substrate of knowledge does undercut familiar approaches to teaching, but this is as much a result of instructional design as anything. AI requires a new paradigm for teaching, one that relocates the struggle necessary for learning to new tasks, new epistemic domains.

The Blank Page

Think of the blank page experience. It has been said that struggling to find those first words to fill a blank page is essential for learning how to write and think. But is it? Were humans unable to think before the invention of writing? Certainly not. Thinking looked different in the oral tradition; notions such as objectivity and reason in the modern sense emerged only after the invention of stable text to capture and share ideas. But humans certainly were able to think prior to the invention of text.

And they will be able to think after the arrival of AI, if we can step back from habit and tradition long enough to consider how we might relocate the struggle and effort essential to learning. When the blank-page experience can be circumvented by asking Claude why Shakespeare’s Hamlet is an important text, educators need to shift that pregnant moment toward another aspect of engaging with Hamlet.

Contrary to popular thinking, doing so isn’t selling out. It is finding a new way for human creativity and critical thinking to express themselves in the service of learning.

“OK, so you have an authoritative-sounding perspective on the significance of Shakespeare’s Hamlet. Let’s unpack it together. Let’s evaluate the claims through the critical lens we’ve adopted in this class. What is this lens, and how does the answer you got from GPT measure up? Give me your analysis.”

That’s non-trivial labor that promotes real learning. Is it the blank page? Of course not. It’s a move analogous to Plato’s use of written dialogue and dramatic context to re-create the oral engagement that Socrates himself lamented would die with the invention of writing (a move that shifted intellectual labor to a new domain in response to technological change).

Socrates, we now see, had a different frame of reference than his student, Plato.

Frames of Reference

A frame of reference is an orientation that influences how we interpret something. It consists of deeply rooted assumptions and is shaped by our values and experience. For example, if you assume that people are inherently selfish and that all social engagements are transactional, you will have a particular interpretation of an act of giving. If I give you a gift, you automatically assume I want something in return.

Someone with a different set of assumptions about human nature will view the very same act and assign it a radically different meaning. Suppose this person has had a caring and supportive upbringing and assumes that people can be authentically generous. To them, receiving a gift doesn’t necessarily mean the person wants something in return; it may just as well mean they are kind.

The act is the same, but it is interpreted in very different ways.

Something similar is true of AI technology. From the perspective of longstanding academic values and traditions, using AI to fill the blank page appears to be a clear act of cheating or misuse. But from the perspective that AI technology is shifting the epistemic landscape, this shift indicates that educators must rethink where and how meaningful struggle now takes place to promote learning.

AI Is Not the Problem

If one were to apply these reflections to my colleague’s report about her faculty (a conversation really about all generative AI), the response would be an exercise in finding common ground:

I understand where you’re coming from. Measured against our longstanding assessment practices, the use of AI can subvert education. Maybe there is another perspective, though.

What if the tools themselves don’t incentivize student cheating unless we insist on doing things the way we’ve done them for generations? What if the technology is an invitation to change the instructional paradigm and apply our expertise to finding new types of meaningful struggle?

In this context, some familiar skills may remain important, while others may not. Literacy could remain central, but collaboration with AI might also require a different model of the relationship between humans and their tools.

The future of instruction lies here, in reenvisioning our craft to build greater human capacities on the shoulders of AI giants. Doing so depends on a willingness to adopt a new frame of reference, and that requires understanding the assumptions at the root of resistance.

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