Coding activity
AI Explorers
Train a pretend 'robot brain'! Show it 5 cat pictures and 5 dog pictures, then test if it can guess a new one.
Materials and setup
- picture cards for sorting
- paper
- pencil
- markers
How this changes by age
Pre-K (ages 3–4)
Train a pretend 'robot brain'! Show it 5 cat pictures and 5 dog pictures, then test if it can guess a new one.
Steps
- Lay out 5 cat pictures and 5 dog pictures in two rows on the floor.
- Play 'Train the Robot': a family member pretends to be the robot. Point to each picture and say 'cat' or 'dog' — that's how it learns!
- Show the robot a new picture you haven't shown before. Can it guess: cat or dog?
- The robot learns from EXAMPLES, not rules. You didn't tell it 'cats have pointy ears.' It figured it out from the pictures!
- Try again: show 5 red objects and 5 blue objects. Can the robot sort new objects by color? Computers learn from lots of examples, just like our game!
- Tell a grown-up one thing that surprised you.
Learning objectives
- Understand that some machines can learn from examples
- Practice the concept of training with data (examples)
- Distinguish between learning from rules and learning from examples
Kindergarten (ages 5–6)
Explore how AI works through games: pattern recognition, prediction, and recommendation systems. Understand that AI learns from data.
Steps
- Pattern game: show a sequence (red, blue, red, blue, ...). What comes next? You just did what AI does: predict from patterns!
- Recommendation game: if someone likes bananas and strawberries, would you recommend an apple or a steak? That's how Netflix recommends shows!
- Play '20 Questions' with an AI twist: the guesser narrows down by asking yes/no questions. AI uses similar decision trees.
- Discuss where AI is in your life: voice assistants, auto-correct, photo filters, and suggested videos.
- Draw a picture of a helpful robot and write what you'd want it to learn to do.
- Tell a grown-up one thing that surprised you.
Learning objectives
- Recognize AI applications in everyday technology
- Understand prediction and recommendation as AI concepts
- Grasp that AI systems learn from data and patterns
Early elementary (ages 6–8)
Study how AI learns: training data, classification, decision trees, and the difference between AI, machine learning, and traditional programming.
Steps
- Compare traditional programming (give rules) vs. machine learning (give examples): sorting fruits by writing rules vs. showing many examples.
- Build a decision tree on paper: classify animals by asking yes/no questions (Does it fly? Does it have fur? Is it bigger than a cat?).
- Discuss training data: if you only showed an AI pictures of golden retrievers, would it recognize a poodle as a dog? Why quality and diversity of data matters.
- Explore AI bias: if training data is incomplete or unfair, AI can make unfair decisions. Give a simple example.
- Try an AI experiment: play a prediction game where you track whether AI can guess your next choice based on your pattern (always choosing the same thing vs. being random).
- Write about the future: what would you want AI to help with? What should AI NOT be allowed to do?
- In one sentence, tell a parent or sibling what surprised you today.
Learning objectives
- Distinguish between traditional programming and machine learning
- Build and use decision trees for classification
- Understand the importance of training data quality and bias
Upper elementary (ages 8–10)
Investigate advanced AI concepts: neural networks, natural language processing, computer vision, and a few big questions about how AI should be used.
Steps
- Study neural networks simply: inputs, hidden layers, and outputs. Draw one and trace how data flows through it to make a decision.
- Explore natural language processing: how does autocomplete predict your next word? Create a simple word prediction system using frequency charts.
- Study computer vision: how does facial recognition work? Draw the steps: capture image, detect face, compare to database, match or not.
- Investigate AI ethics: privacy concerns with facial recognition, algorithmic bias, and where people may still want human judgment involved.
- Compare a few situations where AI can help people and a few where extra caution is needed. Explain your reasoning in a chart or short paragraph.
- Write a short reflection: 'The Future of AI' covering current capabilities, potential benefits, risks, and one rule or guideline you think matters.
- In one sentence, tell a parent or sibling what surprised you today.
Learning objectives
- Explain neural networks, NLP, and computer vision at a conceptual level
- Analyze ethical implications of AI in society
- Evaluate both benefits and risks of AI development
Safety and evidence note
Read the full activity before beginning. An adult should supervise tools, heat, food, outdoor work, movement, and experiments as appropriate. Completion records that the activity was done; the child’s explanation, work sample, photo, or demonstration is stronger evidence of learning than a completion check alone.
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