Last week a young father told me, “We have enrolled our son in a coding class.”
I smiled and asked him, “Is he learning to write code, or is he learning to think?”
He looked confused. For many parents today, the two sound the same. They are not.
Today, any AI tool can write code for you in seconds. So if our children only learn to use code, they will always be behind the machine that writes it faster.
What our children really need is something else. They need to learn to break code. To look at any set of instructions — a recipe, a rule, a homework answer from AI — and ask: What is the pattern here? Where is the mistake? What is missing? What did it choose to ignore?
I call this raising a code-breaker. And you don’t need a computer to do it. In fact, it is better without one.
The Difference Between Using and Breaking
Conventional coding classes often teach syntax — how to type commands. But the foundational skill behind computer science is not syntax, it is computational thinking: decomposition, pattern recognition, abstraction, and debugging (Wing, 2006; Bers, 2021).
When a child drags blocks on a screen, they can succeed by trial and error. When they have to explain an idea to you — and you pretend to be a foolish robot who only follows exact words — they have to reason. Research on unplugged learning shows that early computational thinking develops more robustly through embodied, screen-free activities than through interface-driven trial-and-error (Relkin et al., 2021).
In our own home, we learned that when there is no screen between us, children think more clearly. They look at you, not at the screen.
Game 1: The Buggy Recipe
Take a piece of paper. Write a recipe for making a jam sandwich, but write it wrong on purpose.
I write it like this:
- Put jam on the bread
- Put the bread together
- Get the bread from the kitchen
Then I give it to the child and say, “I am a robot. I will follow this exactly. No guessing.”
They immediately shout, “But you can’t put jam if you haven’t brought the bread!”
I say, “Then fix my code.”
And they rewrite it, step by step, in the right order, with clear words. Sometimes I do it with chai. I once put the tea bag outside the cup because their instructions said “put tea bag on table.”
We laugh a lot. And in that laughter, they learn debugging — the most valuable skill today. Studies of debugging show it strengthens persistence and causal reasoning, because children learn that failure is information, not identity (Bers, 2021).
Key takeaway: A computer does not understand intention, only instruction.
Game 2: The Laundry Algorithm
This one I learned from my own laundry pile.
Put a mixed pile of clothes on the bed — socks, shirts, trousers. Tell your child, “We are going to make a sorting machine. But our machine is very simple. It can only ask questions that have a YES or NO answer.”
Ask them, “What should be the first question?”
Most children will say, “Is it blue?” I say, “If it is blue, where does it go? What about tomorrow when we have red clothes?”
Slowly they learn to ask better questions. “Is it worn on the feet? Is it for the top of the body or the bottom?”
Make a small tree on paper with them:
Is it a sock? YES -> Sock pile. NO -> Is it for legs? YES -> Trousers pile.
This is abstraction — learning what to ignore and what to keep. And pattern recognition — learning what repeats. These are core to algorithmic thinking and are exactly what children need to understand how AI sorts and classifies (Wing, 2006).
Key takeaway: Algorithms are choices about what to ignore and what to care about.
Game 3: The Treasure Map
Hide a small toy in the house. Now tell your child, “I will not tell you where it is. I will only give you three exact instructions.”
You will find that “Go to the bedroom” is not enough. Which bedroom? How many steps? Turn left or right?
Help them break the big task “find the toy” into small, exact steps:
- Walk 10 steps to the hallway.
- Turn left.
- Look under the blue cushion.
Now you hide it and give them instructions that have one extra, useless sentence: “Walk 5 steps, remembering that elephants are grey, then turn right.”
They will ask, “Why did you say elephants are grey?”
I say, “That was extra information. A smart thinker knows what to ignore.”
This is decomposition — breaking a big problem into small solvable parts. It is also the key to prompting AI well. A child who can decompose can guide AI; a child who cannot will be guided by it.
Key takeaway: A smart thinker knows what matters and what does not.
What Peer-Reviewed Research Shows
Unplugged computational thinking games improve logical sequencing, conditional reasoning, and debugging without requiring screen time or syntax mastery (Relkin et al., 2021). Early childhood research emphasizes that embodied, social play with caregivers builds algorithmic habits more durably than solo screen-based coding apps (Bers, 2021).
Wing’s foundational framework (2006) argues that computational thinking is a general human problem-solving habit, not just a technical skill. Teaching children to abstract, decompose, and debug in everyday contexts transfers to digital contexts later.
What Parents Can Do for Algorithmic Thinking
- Protect precise language — Play robot. Follow instructions literally. Let your child fix the bug.
- Protect sorting — Let children create their own YES/NO decision trees for toys, books, or laundry.
- Protect breaking down — Any big task (clean room, set table) can become three small steps. Ask: What is step one?
- Protect ignoring — Add silly, irrelevant sentences to instructions. Let children practice saying, “That’s extra, I will ignore it.”
A Hopeful Conclusion
We do not need to fear that AI will write code. We should celebrate it. It frees our children from typing syntax so they can focus on thinking.
A code-user learns to obey the machine. A code-breaker learns to question it, fix it, and lead it.
When you play the Buggy Recipe tonight, you are not just making a sandwich. You are raising a child who will look at any intelligent system — whether a homework app or a future robot — and calmly ask:
What are the steps?
What is the pattern?
What did it leave out?
Where is the bug?
That child will not be replaced by AI. He will be the one who makes AI better. And that is a hopeful future we can build together, one game at a time. We are with you in this.
What About You?
Have you tried teaching your child to give exact instructions? What funny bug did you discover in your recipe? Share below — we read every comment.
About the Authors
Dr. Shaheen Pasha is a Professor of Special Education with over 35 years of teaching, research, and academic leadership experience. She served as Professor and Chairperson of the Department of Special Education at the University of Education, Lahore. She earned her Ph.D. in Special Education from the University of Southampton, UK. She has published more than 35 research papers and co-authored two books. Her expertise lies in child development, cognitive growth, and special education.
Dr. M. Anwar-ur-Rehman Pasha (widely recognized as Dr. M. A. Pasha) is a Professor of Computer Science with over 35 years of post-graduate teaching, research, and educational management experience. He earned his Ph.D. in Computer Science from the University of Southampton, UK in 1996. His research focuses on Artificial Intelligence, Human-Computer Interaction, and Computational Thinking. He has published two books and over 30 research articles.
Together, they bring 70 years of combined wisdom to help parents raise capable, thoughtful children in the age of AI.
References
Bers, M. U. (2021). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge.
Relkin, E., de Ruiter, L., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young children. Child Development, 92(1), e397-e422.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.