How Professors Are Rethinking AI Literacy in Marketing Education

When Anthropic announced that Claude Sonnet 4.5 could run autonomously for thirty hours, it raised a simple but awkward question for education.If a model can plan, code, and optimise for longer than any human can stay awake, what should business schools still be teaching?

This is not a philosophical puzzle; it’s a curriculum one. Across marketing departments, professors are rushing to include “AI content” in their courses. Most now have at least one class where students use ChatGPT to write copy or design creative ideas. It’s engaging, accessible, and immediately practical.

But as a model of AI literacy, it’s incomplete. Knowing how to prompt a chatbot is not the same as knowing how to manage a system that makes decisions.

From tools to thinking

Marketing academics have always adapted quickly to technological change. When search and social advertising took over, courses evolved. Now, with generative AI embedded in every major ad platform, the question is not whether to teach it—but how.

Many current syllabi equate AI literacy with tool proficiency. Students learn how to use ChatGPT, Copilot, or Jasper to save time. They produce outputs. They learn to prompt efficiently.

That’s helpful, but it treats AI as a calculator: an instrument to use, not an environment to understand.

AI literacy, as we’ve seen it develop in industry, means something broader. It’s about recognising what information the model is drawing on, what assumptions sit beneath its output, and when the automation should be questioned or overridden.

It’s not a technical skill set. It’s a managerial one.

Why this matters in the classroom

Employers don’t ask for graduates who “know ChatGPT.” They look for people who can evaluate AI recommendations, interpret data shifts, and connect algorithmic results back to brand strategy.

That is where traditional digital-marketing education starts to feel dated.When campaign automation handles bidding, targeting, and optimisation, the human value moves upstream—toward framing objectives and judging trade-offs.

This is the heart of what we’ve called in our work the automation paradox.The more marketing automates, the more human judgement matters.

For educators, the challenge is how to make that judgement visible. You can’t teach it through slides or readings. It needs to be experienced.

The experiential turn

That’s why more business-school faculty are turning to simulation-based learning.Simulations allow students to see automation at work, not just hear about it.

In Novela’s Google Ads Simulation, for example, students manage campaigns in a realistic environment. They adjust audiences, budgets, and creative strategies, then watch as AI optimisation reshapes performance. They experience what “machine learning” really feels like—unpredictable, data-driven, and sometimes counter-intuitive.

In the Meta Ads and Organic Social simulations, they learn how recommendation systems prioritise engagement, how creative fatigue appears, and why small data changes cascade through results.

In the AI Marketing Simulation, they collaborate with an AI copilot that proposes decisions, but the human still chooses. Students learn to weigh automation against judgement in real time.

These experiences turn theory into intuition. Students begin to anticipate what the AI might do next—and to understand why.

What AI literacy actually looks like

From the work we’ve done with business-school partners, four principles consistently define effective AI literacy in marketing education:

1. Transparency Students should understand how AI systems make decisions, even if they can’t see every layer of the algorithm. Teach them to ask: “What data is driving this?”

2. Judgement AI is confident even when it’s wrong. Students must practise questioning results, validating performance metrics, and identifying when automation over-optimises for the wrong thing.

3. Ethics and Bias Marketing data reflects human behaviour, with all its biases. AI inherits those biases at scale. Students need structured reflection on fairness, representation, and accountability.

4. Reflection The deepest learning happens after action. Post-campaign analysis—asking what worked, what didn’t, and why—builds the metacognition that defines expert marketers.
   

These ideas form the backbone of our free guide, How to Teach Real AI Skills to Grad Students, which sets out frameworks, classroom activities, and reflection prompts drawn from real implementations.

Designing for the next cohort

Many professors worry that introducing AI content requires rewriting the syllabus.It doesn’t. Often it’s a matter of reframing existing assignments.

For example:

• Instead of asking students to “create a Google Ads campaign,” ask them to interpret one already optimised by AI.

• When students analyse campaign data, include a prompt that asks, “What would you change if this decision came from a model rather than a person?”

• Pair students with your own AI copilot—let them question its reasoning, not just its results.
  

Each of these tasks shifts attention from execution to understanding—from how to use AI to how to think with it.

A broader perspective

There is a parallel here with how medical schools teach diagnosis. Students practise with simulations, observe outcomes, and reflect on their decisions. Marketing education is catching up.

As AI takes over repetitive work, the new professional advantage will be cognitive: the ability to reason about systems that learn. That skill cannot be developed through lectures alone. It has to be played out.

Simulation provides a safe arena for that play. Students make strategic decisions, see real data, adjust course, and learn from feedback.

The value for professors is equally clear. Simulations compress time, expose cause and effect, and let educators assess the thinking process, not just the final result.

The path forward

AI is now the air that modern marketing breathes. The question is not whether to teach it, but how to teach it meaningfully.

Prompt engineering, while useful, is only the surface. True AI literacy lives in the moment when a student looks at an automated result and asks, “Do I trust this—and why?”

That moment is what Novela’s simulations were built to create.

To learn more about how professors are designing courses that prepare students for AI-driven marketing, download our free guide, How to Teach Real AI Skills to Grad Students, or book a short demo to see the simulations in action!