01
Decomposition
Breaking a problem into parts small enough to reason about. The single most transferable skill in the curriculum, and the one students find hardest.
Curriculum / NanoSkool
Computational thinking
A way of thinking that happens to be useful with a computer \u2014 not a coding syllabus with a broader name.
01 — The four moves
What we actually teach
02
Pattern recognition
Noticing what repeats, what is similar, and what has been solved before — so effort goes into the genuinely new part.
03
Abstraction
Deciding what matters and what can be ignored. Judgement, not procedure, and the reason this cannot be taught by drill.
04
Algorithms
A sequence of steps precise enough that something else can follow them. Where the thinking becomes testable.
02 — AI literacy
Age-appropriate, and honest
Students are growing up with systems that answer questions confidently and are sometimes wrong. AI literacy in this curriculum is not about tools — it is about understanding what these systems are doing, where their limits are, and why a strong mathematical and logical foundation matters more rather than less.
For younger students that means recognising intelligent systems in the world around them. For senior students it means working with libraries, IoT and models directly, and being able to say why an output should or should not be trusted.