Thinking Like a Computer (So You Can Talk to One)
The four thinking moves that separate gold from garbage when you use AI.
- Grades 8–12
- 60 minutes
- live clicker activities
The hook
“How do you eat an elephant? The four thinking moves computer scientists use to crack hard problems are the same skills that separate people who get gold from an AI from people who get garbage.”
What students learn
- Decomposition: break a big request into sub-tasks a model can do.
- Pattern recognition: know what AI is good and bad at, and teach it by example (few-shot prompting).
- Abstraction: strip a request to its essentials and specify what matters.
- Algorithm design: give the model a precise procedure, and make it think step by step.
- Why a good prompt is an act of clear thinking, externalised.
The promise
Students leave with the four pillars of computational thinking reframed as prompt-craft: the difference between treating a chatbot like a slot machine and treating it like a tool.
Lands on
The four pillars are the core skills of working with AI. Clear thinking is what turns a chatbot into a tool.
The interactive AI demos
Every idea is a Mirror Move
We run it on the room, show it inside the machine, prove it on a live chatbot, then name the skill. This session's loops:
The hour, beat by beat
- 1
The four pillars
Decomposition, pattern recognition, abstraction, algorithm design, and why they are prompt-craft.
- 2
The peanut-butter robot
Write instructions so literal the teacher “crashes”: an algorithm lesson with no laptop.
- 3
Binary search
Guess a number 1–100 in ~7 guesses; does the chatbot search efficiently too?
- 4
Explore vs. exploit
When to keep iterating on a prompt and when to commit to the winner.
- 5
The whole point
Computational thinking turns a chatbot from a slot machine into a tool.
Drawn from
- Wing — “Computational Thinking” (CACM, 2006)
- Papert — Mindstorms
- Christian & Griffiths — Algorithms to Live By
- Hillis — The Pattern on the Stone
- CS Unplugged (Bell et al.)
Sources are paraphrased; quotes are short and attributed. We teach the same scepticism toward AI as toward any confident claim.
Next session
How Machines Learn (and Why They Get It Wrong)
Know the machineReady to book?
Run this session standalone, or as part of the four-session package.