2018. I’m standing with a group of foundry operators at Krakodlew in Kraków. They’re about to put on VR headsets for the first time in their lives. One of them — 20 years at the furnace, deeply experienced — tells me: „This is for young people. I know what I’m doing.”
Two hours later, that same person asks me when the next session is.
Teaching technology — especially AI and AI-powered tools — follows the same pattern. The entry barrier is psychological, not technical. And when you break through it with the right method, the rest comes surprisingly quickly.
Over the last several years I’ve worked with people who had never written a line of code: machine operators, production managers, executives, vocational school students. Here’s what actually works.
Mistake #1: start with the technology
Most AI courses and workshops begin like this: „Today you’ll learn about language models. An LLM is a model trained on large datasets…”
You lose the learner by the fourth sentence.
Adults learn differently from children. They don’t absorb abstractions first and then search for applications — they absorb concrete experience first and then find theory to explain it. Andragogy (adult learning theory) has been saying this since the 1960s. The problem is that most AI instructors are engineers who teach the way they themselves learned.
Rule: start with a problem the participant has today. Not with the technology you want to show them.
At the foundry, we didn’t start with „how VR works.” We started with: „Let me show you a situation where an accident happened at a similar plant. How would you respond?” The technology became a tool for solving a specific, real problem. Not a demonstration.
The framework that works: „Touch first, explain after”
Through VR training, I developed a sequence I call „touch first, explain after.” It works exactly the same in teaching AI and vibe coding:
- 1. Do it together — don’t explain first. Give the participant a simple task to complete immediately. A mistake in a safe environment teaches more than an hour of lecture.
- 2. Name it together — when something works (or doesn’t), ask: „What just happened? Why do you think that is?” The learner builds a mental model from their own experience.
- 3. Now explain the mechanism — only now do concepts like „token,” „context,” „temperature” have meaning. Because you have a concrete situation to attach them to.
- 4. Give them a problem to solve independently — not „an exercise with instructions.” A problem you haven’t walked through with them before.
These numbers are from VR training, but the pattern is the same: when you teach through doing, not through listening, outcomes are dramatically different.
What blocks AI learning — and how to remove it
Teaching AI and vibe coding, you’ll encounter the same barriers repeatedly:
„This isn’t for me, I’m not technical enough”
Antidote: show a specific example of someone similar to the learner who’s already doing it. Not Elon Musk. Someone who runs a small business and uses Claude to write proposals. Someone who’s an operator and learned to program a robot in a course. Identification before aspiration.
„I don’t know what to say to AI”
Antidote: give them templates. Not as rigid forms, but as starting points. „I am [role], I’m trying to [goal], I have a problem with [specific].” Learners need scaffolding before they build their own prompting style.
„How do I know if the answer is correct?”
This is the best barrier, because it’s legitimate. Critical thinking toward AI output is a key skill — not an obstacle to learning. Teach the learner to verify: cross-check with another source, test „does this make sense in my context,” ask AI a follow-up („check whether you made an error in assumption X”).
The best AI learners aren’t those who trust it unconditionally — they’re those who can hold a dialogue: ask, verify, correct, iterate.
Robotics as the entry point to AI for younger learners
At XR FabLab in Chrzanów, we worked with vocational high school students. The threshold for abstract „AI” was too high. Robotics turned out to be the perfect bridge.
Why robotics works:
- Immediate feedback loop — the robot moves or it doesn’t. No ambiguity. The student sees the result of their decision within seconds, not days.
- Physical artifact — they can touch it, show it to parents, break it and fix it. Code on a screen is abstract. A robot is real.
- Programming as commands — students intuitively understand a sequence of instructions. It’s a direct analogy to prompting AI.
- Competitive element — races, challenges, leaderboards. External motivation until intrinsic motivation develops.
When a student understands that the robot does exactly what they told it to do — no more, no less — they also understand why precision in prompting matters. That’s the transfer moment.
What separates a good AI instructor from a weak one
A good AI instructor isn’t someone who has the API documentation memorised. It’s someone who:
- Remembers what it felt like to not know — and can genuinely inhabit the learner’s perspective
- Gets excited when a student surprises them with a new application of the tool
- Treats a student’s mistake as diagnostic information, not failure
- Is still learning themselves — AI changes every few months, and standing still means falling behind
The most important sentence you can say to a learner: „I don’t know, let’s find out together.” That models exactly the kind of thinking AI requires.
A practical starting framework
If you’re designing an AI course or workshop from scratch, one structure I’d recommend:
- Session 1: Participant’s problem → first contact with the tool → surprise (good or bad) → shared analysis
- Session 2: Building vocabulary → prompt templates → first independent task
- Session 3+: Participant’s own project → iteration → presenting the result
The own project is critical. When a participant solves their own problem — not a practice exercise — and AI helps them do it, that’s when the tool becomes theirs.
Who in your organisation or classroom is hardest to convince about AI — and what’s their main barrier?
Author has been implementing VR training in industrial environments since 2018 (Krakodlew, XR FabLab Chrzanów). Teaches AI, automation and AI-assisted coding in practical contexts — in Poland and internationally. Sources: PwC VR Soft Skills Study 2020 (n=10,000); own data Krakodlew/Industrverse 2018–2024.