From Knowing to Learning
Week 1
Don't we all have a junk drawer in the kitchen? Full of spare parts, mystery cables, and bits and pieces we've been saving for that one day we might need them.
Well, this is that day.
This week we're having a good old rummage. Everything you've picked up about AI, the bits you've half tried, the things you've heard, the stuff you've been meaning to look into — it all counts. Let's see what you're actually working with.
This week is about finding out what's already in your drawer.
This week, you're going to use AI to challenge your expertise. Not because AI is smarter than you (it's not). But because the act of inviting challenge (instead of seeking confirmation) is how you practice the learning mode.
Pick one specific topic within your domain where you feel confident. Not 'my whole job', something specific. Like how you plan a roster, write a safety report, schedule a maintenance run, draft a project update, or structure a campaign. You need to feel like you're in knowing mode to practice switching to learning mode.
Then, ask AI to show you what you're missing.
Choose your track:
Pick one specific topic within your domain where you consider yourself experienced. Use an AI tool (ChatGPT, Claude, Gemini, etc.) to ask it to challenge your thinking.
Choose one of these three prompt templates:
"I've been working in [specific domain] for [X years]. I believe that [specific assumption or approach you hold]. What am I missing? What would someone from [different discipline: psychology, economics, design, etc.] notice about this that I might not see?"
"Here's how I currently approach [specific task or problem]: [describe your approach in 2-3 sentences]. What are the weaknesses in this approach? What could go wrong that I'm not anticipating?"
"I typically solve [specific problem] by doing [your usual method]. What are three completely different ways to approach this that I probably haven't considered?"
Pick one specific challenge your team is currently facing. Not "we need to be more innovative", something like "we're deciding whether to rebuild our legacy system or patch it."
Use an AI tool to generate questions that expand how you're thinking about the problem.
Choose one of these two prompt templates:
"My team is facing this challenge: [describe in 2-3 sentences]. I've been focused on [the aspect you're currently focused on]. What questions should I be asking that I'm probably not asking? What am I not considering?"
"Here's a decision my team needs to make: [describe the decision]. Here are the assumptions I'm making: [list 2-3 assumptions]. What assumptions am I making that I haven't named? What would need to be true for this decision to backfire?"
If AI output feels unhelpful, try these:
⚡ Quick tips
Start specific. Refine, don't restart. Use disagreement as data. Ask meta-questions: "Why did you interpret it that way?"
After you complete your experiment, it's time to process what happened. This is where the learning actually sticks.
Your AI reflection coach
💡 Pro tip: Start your reflection with this prompt:
"Eko, I just completed my Week 1 experiment for the AI Starter Sprint. I practiced the learning mode by asking AI to challenge my thinking. Can you help me reflect on what I noticed?"
Not required, for those who want to go deeper.
Dweck's research on mindsets is the psychological foundation for the "knowing vs. learning" distinction. Focus on how praising intelligence creates rigidity, while praising effort creates curiosity.
▶️ Watch on YouTube →The real danger isn't the beginner who thinks they're an expert. It's the expert who stops questioning their expertise. This one will make you think about where you might be sitting at "peak confidence."
▶️ Watch on YouTube →Expertise creates efficiency but also blindness. You stop seeing what beginners see. This piece explores how that happens, and this week is about recovering that sight.
📄 Read the Article →