You know that feeling when your students casually drop, “Oh yeah, I ran that prompt through ChatGPT already” before class even begins? It’s like AI is no longer a guest in the classroom, it’s already part of the furniture. In moments like that, it’s tempting to throw up your hands and scramble for brand-new theories. But recently I’ve been turning back to Marshall McLuhan, and reminding myself and my students just how powerful his insights still are.
What’s funny, or perhaps reassuring, is how McLuhan, in many ways, anticipated what we’re wrestling with now: not just new media, but new modes of attention, authorship, and ultimately power. And when I pair him with Vilém Flusser, whom I’ve written about in relation to Günther Anders in this article, I get a richer pedagogical toolkit for teaching media in times of generative AI. One of the first things I tell my students is: don’t get stuck in the what-does-this-AI-output-say trap. Because McLuhan asks something more radical: what does the medium itself do to us? In class, when an AI spits out a “perfect” paragraph, I pause and ask: who is doing the thinking now? Where does human insight enter, or even escape? I’ve seen students shift from complainers (“Well, the essay is plagiarized”) to explorers (“Wait, how did the AI choose this phrasing?”). That shift, from policing content to interrogating process, is exactly what makes McLuhan’s dictum “the medium is the message” feel freshly urgent again.
It’s tempting to focus on whether the AI got the facts right, or whether it’s too cookie-cutter. But McLuhan trained me to see that content is often a smokescreen. The real action is in how the tool shapes cognition and perception. So I ask students: where did the AI get stuck or overreach? What biases leak into its “knowledge”? What does it not say? We compare versions, reverse engineer prompts, and probe the logic that the machine seems to follow. Over time, students begin to see that an AI output is less a product than a negotiation with a medium. That realization alone is, I think, a new form of literacy.
My classes often take on a workshop feel. With AI, that means asking students to generate a text and then “play it against the grain”, subvert it, distort it, push it beyond its limits. Sometimes we “translate” a machine output back into a human voice and reflect on what changed in the process. Other times we stage debates where one side defends what the AI “thinks” and the other challenges it, treating the machine like a strange interlocutor. These activities make the invisible environment visible, which is exactly what McLuhan wanted education to do.
I also borrow from McLuhan’s self-description as a “pattern recognizer”. More and more, I feel this is the teacher’s role today. In an environment flooded with machine-generated text and data, I can’t (and shouldn’t) compete with AI in speed or volume. But I can help students notice patterns, what gets repeated, what gets excluded, what biases become normalized. When a student points out, “Whoa, this AI avoids talking about XY,” I help them trace that back to training data, sources, corporate interests, and algorithmic design. The point is not to out-perform the machine, but to read the landscape it creates. This connects directly with Flusser’s insights, which I explored in my article The Relationship between Vilém Flusser and Günther Anders in Media Studies and Politics published in Flusser Studies 39. Flusser encourages us to see media not only as instruments of control but also as “playthings” that can be turned against the apparatus. That lens has been invaluable in my teaching. If students see AI only as a villain, they remain passive. But if they see it as something they can play with, bend, and repurpose, they become active agents.
At the same time, Anders’ warnings remind us not to forget the alienation, the risks, and the loss of agency technologies can bring. Holding both perspectives together helps me avoid the extremes of naïve optimism and pure techno-pessimism. In the end, what I want my students to develop is not just technical fluency but what I’d call AI literacy. They need to understand AI as an extension of human faculties like memory, imagination, and authorship, and, at the same time, also recognizing the invisible infrastructures and power relations that shape it.
McLuhan gives me language to talk about media as environments and extensions; Flusser gives me ways to talk about play, resilience, and subversion. Together, they give me a framework for guiding students to read, critique, and even reprogram the media systems that surround them. I don’t keep McLuhan in my syllabus out of nostalgia. I keep him because his insights still crack open new questions for me and my students. He reminds me that communication is never neutral, that classrooms must adapt to the media they host, and that teaching is ultimately about helping students perceive patterns in a shifting landscape. So when a student shrugs and says, “I just let ChatGPT write this” I don’t panic. I see it as a portal. We jump into the ecology of the medium, probe its assumptions, and ask what it wants from us. In those moments, McLuhan is alive and well in the age of AI, and my students are learning how to talk back to the machine.
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