Turning AI from an Answer Machine into a Thinking Tool
In Week 1 this semester, I asked my sport marketing class a question.
No hands went up. Instead, there was the faint hum of typing. Laptops are angled slightly away from me. A few grins. Within seconds, ChatGPT was speaking…through my students.
Not once. Not twice. Almost every question.
They weren’t hiding it. For them, this wasn’t cutting corners — it was just how things are done.
That left me with two choices:
Ban devices and spend the semester playing academic whack-a-mole, or
Lean in, and teach them how to work with AI while keeping the strategic thinking firmly in their hands.
I chose the second.
A model that makes sense
Danny Liu’s recent article on the University of Sydney’s “two-lane” approach to assessment put words to what I’ve been trying to do.
Lane 1: secure, in-person assessments where AI use is controlled or excluded — the place to see what students can do on their own.
Lane 2: open assessments where AI use is assumed, encouraged, and part of the brief — but only if it’s applied effectively, transparently, and with critical judgement.
The key shift?
From “Are students using AI?” to “Are they learning?”.
Sport marketing needs both lanes. Graduates must be capable without AI, but they must also be fluent in using it well, because that’s the reality of the industry they’re heading into.
The Workshop: a weak market on purpose
We’re working with real case studies this semester — Auckland FC, Golf NZ, Moana Pasifika, and Hyrox.
I gave each group a deliberately broad, strategically flat target market.
For Golf NZ: “Anyone in New Zealand who plays golf.”
It’s technically correct. But it’s lazy segmentation — the kind that can’t sell sponsorship, grow participation, or guide campaign messaging.
The brief had three steps:
Refine the market — make it specific, relevant to the case, and aligned with segmentation principles.
Run it through AI — ChatGPT, Claude, Perplexity, Gemini, whatever they liked.
Evaluate the AI output — using the quality criteria we’d established earlier.
What I saw in the room
This is where the classroom lit up.
One table had five AI tools open at once, pitting them against each other like rival agencies in a pitch. Their debate over which output had more depth was louder than anything I’ve heard in Week 3 of a course.
Another group stuck with a single AI tool, stared at the underwhelming result, and asked: “So… what’s wrong with it?” That became a perfect opening to talk about vague demographics, missing psychographics, and the absence of any link to the actual product.
And then there was the group who got a beautifully confident, well-structured AI answer… that never defined a market at all. It jumped straight into campaign tactics. It looked right but was strategically empty — a textbook example of why surface-level confidence can be dangerous.
We unpacked every example together:
What’s missing?
Does this align with your case study’s priorities?
What data could make it stronger?
Where would you find it?
By the end, I wasn’t the only one asking these questions. Students were spotting the gaps themselves. They stopped saying “Is this a good AI answer?” and started asking “Is this the right answer for us?”.
Why this matters beyond the classroom
This approach worked because it reframed AI from a solution to a starting point.
In sport marketing, that’s exactly how AI functions in the real world:
A CMO isn’t signing off on the first draft from AI (or an intern).
No campaign is built on “fans aged 18–34” without layers of data, insight, and strategic alignment.
The best work comes when you interrogate the brief, refine the audience, and design something that works for this product, this market, this moment.
If graduates can do this — evaluate, adapt, and justify their work — they’ll be the ones shaping campaigns, not just executing them.
The Bigger Picture for Future Graduates
Liu’s “two-lane” model isn’t just a policy idea. In practice, it’s a blueprint for graduate readiness.
Lane 1 activities keep integrity in check — showing that a student can do the thinking themselves. Lane 2 activities, like this workshop, give them the chance to practise the messy, iterative, tool-rich reality of the modern sport industry.
In both lanes, the end goal is the same: not graduates who can avoid AI, but graduates who can out-think it.
Because in sport marketing, the future belongs to those who can bridge the gap between technology and strategy — who can use AI to accelerate their work without letting it replace their judgement.