Imagine a marketing student who hands in an A level case study. It has a solid situation analysis, competent competitive set, sound positioning, and reasonable recommendations.
Now imagine that same student 3-6 months later. They graduated with a high GPA and landed their dream job. Their manager asks them to analyze why sales have been declining the last year and make a recommendation.
The student freezes. Not because they’re not smart. But because something essential was never built. In the busyness of interviewing, getting ready to graduate and enjoying their senior year the temptation to get the quick answer from an AI prompt was too tempting.
The professor didn’t notice the first time. AI is getting better, AI checkers aren’t always accurate and AI use is more difficult to prove with tools that humanize AI writing. So the student used AI to do all the work for all case assignments. They thought they found the easy way to their dream job.
The thinking that should have happened was quietly outsourced to AI.
But the answers AI provides for well known text and HBR cases aren’t transferable to the unique current situation the company faces. The student didn’t learn to research, synthesize, draw insights, and apply critical thinking. They never learned to empathize with customers. They didn’t learn to use AI in ways to increase their value as an employee.
This is hypothetical, but something I think about as I consider how we teach in an AI-assisted world. The issue wasn’t using AI, it was using AI in the wrong way.
Right now, higher education is pulled between two camps. Prohibitionists see AI as a threat to academic integrity. Accelerationists think traditional learning is obsolete. Both sides are arguing about the wrong thing. The more useful question? When students use AI, is it making their thinking stronger or weaker?
Two books helped me see this more clearly: S.I. Hayakawa’s Language in Thought and Action and Angus Fletcher’s Primal Intelligence. Read together, they point toward a framework that’s more useful than a simple “allowed” or “not allowed” policy.
The Map Is Not the Knowledge
Hayakawa’s reminder, “the map is not the territory,” can apply to how students use AI. In a college course, the final deliverable is just a map. The territory is the cognitive struggle. It’s the connections made while wrestling with a real problem, the moments of confusion that eventually resolve into genuine insight.
In the student hypothetical, the case analysis is the map. The manager’s question about the decline in sales is the territory.
When a student writes a case analysis, the learning happens in the hard questions. Who’s this brand actually talking to? What do they feel when they see the ads and use the product? Are there new competitors? Has the market changed? Does the positioning hold up?
If AI answers all those questions, the student gets the coordinates without building the navigation skill. When that gap appears in the real world, it feels like personal failure. What happened is the thinking was outsourced at exactly the moment it needed to happen.
The grade is the map. The cognitive struggle is the territory. AI can help you understand the map, but only you can travel through the territory.
Your Brain Is Not a Recommendation Engine
This is where Fletcher’s work in Primal Intelligence becomes useful for how we think about student learning.
AI runs on correlation (A = B). It looks at what’s already been written and calculates the most probable next word, the most common next move. It’s a Data Brain that’s incredibly fast, but fundamentally a high-speed echo of the past.
Your brain runs on conjecture (A → B). You don’t just see two things are related. You imagine how one causes the other asking “Why?” and “What if?” in ways a correlation engine cannot.
AI can analyze 500 brand campaigns and tell you the most common recommendation. That’s correlation A = B. But only a student who’s spent time in the original data to draw insights from real consumers can ask: “Why are brands that lean into vulnerability outperforming ones that lead with aspiration?” That’s conjecture A → B. That’s the thinking that builds a marketer.
There is a kind of thinking (imaginative, causal, empathic) that AI cannot do for students. If they don’t practice it, they don’t develop it.
When you focus on the grade using AI to avoid the struggle, you lose the capability.
The 5 Levels of Classroom Integration
Instead of “using AI” or “not using AI,” there’s a more productive question. What level of integration serves the learning objective? Here’s a framework I’ve been developing:
Not every assignment should allow the same level of AI use based on objective and context.
Make the Invisible Visible
A useful tool that could have helped the hypothetical student is an AI Audit Log. Students record which tool they used, what prompts they gave it, what output they received, and how they verified, modified, or built on that output.
An AI audit log makes AI use visible instead of hidden. It makes students slow down and ask, Am I using this to avoid the thinking, or to deepen it? It also shifts the conversation from “gotcha” enforcement to a learning conversation.
You might ask students to log how they used AI to research a target audience, then trace where they went beyond the AI output. What did they verify? What did they challenged? What human insight did they add? The log becomes evidence of the cognitive work.
An AI Audit Log makes the invisible visible. It shows whether a student is building their thinking or outsourcing it.
Moving from “Gotcha” to “Growth”
The detect-and-punish model is understandable, but fights the wrong battle. What’s more beneficial is assignment design that makes the learning objective transparent and specifies which level of AI integration is appropriate.
Instead of: “No AI allowed on this assignment” (vague, unenforceable, adversarial)
Try: “For this brand audit, you may use AI at Level 1 (concept clarification) and Level 2 (brainstorming competitor categories), but Levels 3–5 are off-limits because the objective is to develop your own consumer insight framework. Document in an AI Audit Log.”
What Higher Education Should Develop
The hypothetical student in their first job isn’t underprepared in the traditional sense. They can define positioning and list the steps in the strategic marketing process. What they lack is the practiced habit of executing that process.
They also lack the habit of asking “Why?” when looking at market data. They never learned and practices the imaginative skill of moving from the abstraction down to the lived human experience of the consumer.
That’s when marketing, management and communications education is at its best. When students develop the ability to look at a spreadsheet and see the human story. When they have capacity to read a consumer insight report and sense what’s missing from it. Students who simply use AI to get the answer will never build the skill to make the imaginative leap from what the data shows to what the brand should do next.
AI can tell you what usually works in a category. It can’t tell you what your specific consumer is feeling right now, or why a campaign that followed every best practice still missed. That’s territory. And it requires a brain that has practiced traveling through it.
AI can tell you what usually works (correlation). Only you can imagine what should work next (conjecture).
For students: Look at your last assignment. Did you use AI to avoid cognitive struggle, or to sharpen your thinking? Your thinking skills are either getting stronger or weaker.
For professors: Look at your next assignment. What’s the learning objective? Which level of AI integration serves it? Can you write the instructions to name the level, explain why, and ask for an AI Audit Log?
The goal isn’t to police AI use. It’s to help students understand when they’re building their human brain skills and when they’re weakening them.
In a world where AI handles correlation, the students who know how to conjecture, imagine causal stories the data hasn’t seen yet, are the ones who will be valuable.
About This Post’s Creation
This post was developed in partnership with Claude. I provided the frameworks from Hayakawa and Fletcher, experience from my teaching, and the 5-level scale adapted for education. Claude helped organize and refine.
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