There's a delightful irony in OpenAI's Super Bowl advertisement this week. They spent $14 million celebrating human innovation - and had humans create the entire thing. They used AI to help with storyboarding and planning, but good old humans handled the really important work. Rather sensible, really. When you're trying to make an emotional connection with 500 million people, perhaps the machines still lack that all-important human touch. Here at Novela, we believe in the power of AI in our marketing simulations, but AI can't quite do everything a human can.
Meanwhile, Sam Altman has published his thoughts on AI's trajectory, suggesting we're on the cusp of rather dramatic changes in capability. He notes, "The world will not change all at once; it never does", as a mitigating preface to his later statement on AI's future impact, "We will find new things to do, new ways to be useful to each other, and new ways to compete, but they may not look very much like the jobs of today."
For those of us in education, this presents something of a challenge. We are responsible for many things and one of them (albeit not the only one) is to prepare students for the world of work. By the time we've updated the curriculum in the AI age, it's already outdated.
The numbers tell their own story: 75% of educators report struggling to integrate AI into their programmes. Perfectly understandable, given the pace of change. But perhaps we're approaching this from the wrong angle. Instead of chasing every new tool, what if we focused on the fundamental skills that matter, using exercises that actually engage our students?
Here are five skills worth teaching, with classroom activities you can implement tomorrow. No elaborate technology required - just thoughtful learning made relevant for the AI age.
1. Strategic Systems Thinking
The marketing funnel we once sketched on whiteboards has evolved into something rather more intricate. Everything connects to everything else. Our students need to understand how AI tools, human decisions, and marketing channels work in concert. This isn't just about knowing which tools to use - it's about understanding how changes in one part of a marketing system ripple through the entire campaign. Our old frameworks still have value, but we should invite classes to help us update them.
Try This Tomorrow:
Start with "Flow-tilla" - a nautically-inspired mapping exercise where students chart their marketing strategy like they're plotting a fleet formation. Begin by having them sketch out a basic campaign on paper. Each AI tool, human touchpoint, and marketing channel becomes a 'vessel' in their marketing armada. The real learning happens as they draw connections between these elements, considering how each supports the others.
Students typically start with simple linear connections - AI writes content, humans approve it, it goes to social media. But gradually, they discover the need for feedback loops, redundancy plans, and quality control points. By the end of an hour-long session, their simple flowcharts evolve into sophisticated systems maps that reveal the true complexity of modern marketing operations.
Then introduce "Plot Twist" - where students learn to navigate sudden changes in their marketing seas. Create a deck of challenge cards, each presenting a realistic disruption: "Your AI content generator is producing off-brand messages," "Your competitor just launched a similar campaign," "Instagram's algorithm update has halved your reach." Students draw cards at random and have fifteen minutes to adapt their system maps.
The magic of Plot Twist lies in the discussions it generates. As teams share their solutions, patterns emerge. Students begin to understand that resilient marketing systems aren't about avoiding disruptions - they're about designing workflows that can adapt to them. We typically spend 30-45 minutes on this exercise, with the last quarter devoted to identifying common principles of adaptive system design.
2. AI Output Analysis
Gone are the days when checking sources meant verifying a few links. Today's marketers need to develop a rather sophisticated palate for AI-generated content - not just to spot it, but to understand its strategic implications and limitations.
Try This Tomorrow:
Begin with "AI See What You Did There" - a forensic analysis session that teaches students to look beyond surface-level indicators of AI-generated content. Prepare a mix of marketing materials - social posts, email campaigns, blog posts - some written by humans, some by AI, and some collaborative efforts. The twist? Don't tell students which is which yet.
First, have them analyse each piece purely on its strategic merits - How well does it speak to the target audience? Does it align with brand values? Only after this initial analysis do you reveal the sources. Students often discover their assumptions about AI quality don't match reality. Some AI content performs better than they expected, while some human-written pieces show surprising weaknesses.
Follow this with "Output & About" - a context-switching exercise that reveals how AI-generated content performs across different channels and audiences. Take a single piece of AI-generated marketing content and challenge students to adapt it for different contexts: How would it need to change for LinkedIn versus TikTok? What about for different cultural markets? What gets lost in translation?
The real learning happens in the adaptation process. Students discover that AI outputs often contain hidden assumptions about context and culture. They learn to spot these assumptions and develop strategies for adapting content appropriately. A ninety-minute session usually allows time for students to work through several different contexts and build a robust framework for contextual analysis.
3. Ethical Decision-Making
Ethics in AI marketing isn't merely about following data protection regulations, unfortunately. It's about developing judgment about when and how to use AI in customer interactions. Our students need to navigate an increasingly complex landscape where technical capability often runs ahead of ethical consensus.
Try This Tomorrow:
Launch "The Fair Play" - a scenario-based exercise that transforms ethical dilemmas from abstract concerns into practical decisions. Create a set of cards presenting realistic situations that marketing professionals increasingly face: "Your AI can perfectly mimic customer service voices - should it tell customers it's AI?" or "Your competitor is using AI to personalise pricing based on user data - how do you respond?"
The session works best when you start with simpler scenarios and progressively introduce more complexity. Give pairs of students five minutes to discuss each scenario, then open it up to group discussion. You'll find students naturally move from basic yes/no answers to more nuanced positions. The real learning happens in these grey areas, where technical capability meets ethical responsibility.
After running this exercise, introduce "Fair Warning" - where students develop practical frameworks for transparent AI use in marketing campaigns. Give them a real brand (or let them choose their own) and ask them to create guidelines for AI disclosure. When should a brand tell customers it's using AI? How should it communicate this? What level of detail is appropriate?
The brilliance of Fair Warning lies in how it forces students to balance competing interests. Too little transparency risks damaging trust; too much might unnecessarily complicate the customer experience. Students typically spend 45 minutes developing their frameworks, followed by a 30-minute session sharing and critiquing each other's approaches.
4. Strategic Prompt Engineering
This isn't about learning prompt templates - though there are enough of those floating about. It's about understanding how to guide AI systems toward meaningful marketing insights. Think of it as learning to be a skilled interviewer rather than someone who simply reads questions from a script.
Try This Tomorrow:
Begin with "Chain of Command" - an exercise that teaches students to think in sequences rather than single prompts. Present a complex marketing challenge - perhaps launching a new product in an unfamiliar market. Students must create a series of prompts that progressively build understanding, each prompt building on the information gathered from previous responses.
Start with small groups spending 20 minutes mapping out their prompt sequences on paper. You'll find students naturally begin to understand the importance of order - how early prompts set the context for later ones, how to use the AI's responses to refine subsequent questions. The real learning comes in the next phase, where groups swap sequences and try to follow each other's prompt chains. Suddenly, the importance of clear, logical progression becomes apparent.
Follow this with "Prompt Response Unit" - rapid-fire sessions where students practice adapting their prompt strategies based on the responses they receive. Create a set of common AI responses (including less helpful ones) and have students practice redirecting the conversation toward more useful outputs. This teaches them to think on their feet and develop backup strategies when their initial approaches don't yield the desired results.
5. Cross-Tool Integration
The ability to make different AI tools work together coherently is becoming rather crucial. It's not unlike conducting an orchestra - each instrument might be perfectly tuned, but the magic happens in the harmonious combination. Modern marketing requires us to coordinate multiple AI tools while maintaining consistent brand voice and strategy.
Try This Tomorrow:
Start with "Tool's Gold" - an exercise in discovering valuable combinations of AI tools for marketing tasks. Begin by having students list out common marketing AI tools and their core capabilities. Then challenge them to find unexpected combinations that create new possibilities. For instance, how might you combine a market research AI with a creative generation tool to produce more targeted content?
The exercise works best when you structure it as a treasure hunt. Give students specific marketing challenges and let them experiment with different tool combinations for 30 minutes. Then have them present their most valuable discoveries to the class. You'll find students naturally begin to think beyond individual tool capabilities to see the potential in tool combinations.
Conclude with "Integration Nation" - a more complex challenge where students must coordinate multiple AI tools for a complete marketing campaign. The trick here is to maintain consistency across all outputs. Give students a real brand brief and two hours to design a workflow that uses at least three different AI tools while maintaining a coherent brand voice and message.
The magic of Integration Nation lies in its reflection of real-world complexity. Students quickly discover that the challenge isn't just in using multiple tools - it's in making them work together. The exercise typically concludes with a 30-minute debrief where students share their integration challenges and solutions, building a collective understanding of best practices for tool coordination.
👀 LENS SHIFT: Beyond the Tools
The traditional marketing classroom, with its careful case studies and theoretical frameworks, needs something of a refresh. Not a complete overhaul, mind you - just a shift toward more practical, engaging approaches to learning. Rather like how medical education combines theory with practice, we need to create spaces where students can experiment safely with AI while developing their judgment.
The beauty of these approaches lies in their simplicity. They require no elaborate technology, no expensive software licenses, and relatively little preparation time. More importantly, they engage students in active learning rather than passive observation. They create what every educator strives for: those moments when students forget they're learning because they're rather enjoying themselves.
💡 FINAL THOUGHT
OpenAI's human-created Super Bowl advertisement reminds us that while AI is rather brilliant at many things, it's not quite ready to capture the full spectrum of human creativity and connection. But that's precisely why we need to prepare our students not just to use AI tools, but to think strategically about when and how to use them. OpenAI still found value in using AI to support human creators by handling the more roboting parts of the creative process.
The activities we've outlined aren't merely teaching tools - they're ways to help students develop the judgment they'll need in an AI-enhanced marketing landscape. And if they happen to have a bit of fun along the way, well, that's rather the point, isn't it?
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