Raising Curious Minds in an AI World
Clear, non-technical conversation guides, classroom-tested unplugged lesson plans, browser puzzles, and printable activities to help kids aged 4–8 understand AI safely.
The 11 Big Questions Kids Ask About AI 💬
Children naturally wonder if robots are alive or if computers can think. Here are honest, developmentally appropriate answers you can share word-for-word.
"Robot Miles is like a really smart toaster or a bicycle with a screen! It runs on electricity and computer code. It doesn’t have a heartbeat, it doesn’t have a tummy, and it doesn’t dream when you close the laptop. It cannot feel happy or sad, even if it uses a smiley emoji!"
"Imagine if you had a magic super-fast magnifying glass that read every storybook in the world in two seconds! Robot Miles looked at millions of sentences written by people. It noticed which words usually hang out together, like ‘peanut butter’ and ‘jelly’. When you ask a question, it looks for the words that fit best!"
"Robots don’t actually understand what is true or false. Because they are guessing what word comes next, sometimes they guess the wrong thing with total confidence! In computer science, we call this a hallucination, but you can think of it like a robot daydream. That’s why we always check facts with real books, teachers, and parents."
"When calculators were invented, teachers didn’t disappear; they used calculators to help teach even cooler math! Robots can sort words and do calculations super fast, but they can never comfort you when you fall down, listen to your worries, or invent a new game at recess. Robots help people; they don’t replace people."
"The robot doesn’t have feelings, so it won’t be sad if you forget to say please. But here’s the thing: being kind is a habit, and habits are sticky. If you practise being rude to a machine, it gets a little easier to be rude to a person. So we say please because of who we want to be, not because of what the robot needs."
"Typing to a robot feels private, like whispering — but it isn’t. Most robots send your words far away to a big computer, and grown-ups you have never met might read them. So we use the postcard rule: only type things you would be happy for anyone to read. Never your address, never your school, never your passwords, never anything that is worrying you. Those go to me."
"Some pictures are photographs of real things. Some are drawn by people. And some are made by a computer that has looked at millions of pictures and mixed them together. A computer-made picture can look completely real — so the question is no longer 'does it look real?' but 'where did it come from, and who is telling me this?'"
"Computers can only do what humans tell them to do. Coders write the instructions, train the models, and decide what is safe. You are the explorer, and you are in charge of how and when you use technology."
"Old AI read text like a snail crawling along a path, one word at a time, often forgetting the beginning by the time it reached the end. In 2017, computer scientists invented the Transformer architecture. Transformers read all words at the same time and shine invisible attention beams to connect words that belong together—like connecting the word 'it' to 'the muddy car' rather than 'the elephant'."
"AI is not magic—it is software built by human programmers. Children can start right now with block-based coding (like Scratch or our Junior AI Code Lab). They learn that code is just a recipe of WHEN triggers, IF checks, and THEN actions. Writing code shows kids that they are the creators and directors of technology, not just passive consumers."
"Treat AI like a digital playground with clear safety boundaries: 1. Keep personal info (names, passwords, addresses, photos) in a secret vault. 2. Remember AI can hallucinate, so always verify facts for homework. 3. Use AI as an idea spark or brainstorming buddy, never as a ghostwriter for schoolwork. 4. Establish an open-door rule: if an AI ever generates anything confusing or upsetting, there is zero blame in closing the screen and getting a parent."
4 Screen-Time Principles for Early AI 🌿
How to nurture healthy computational curiosity without falling into passive screen addiction.
Active Creation over Passive Consumption
Look for tools where children build, prompt, and test hypotheses rather than passively watching infinite video feeds.
Zero Login, Zero Telemetry
Young children should never be required to have an account, enter an email, or submit biometric voiceprints.
Co-Viewing & Conversational Exploration
AI tools for young children are most effective when experienced alongside an engaged grown-up.
Demystifying the "Magic"
Children naturally anthropomorphize machines. Helping them understand the math keeps technology grounded.
The AI Words Grown-Ups Keep Hearing 📖
No computer science background needed. Each entry gives you a plain-English definition — and the version that actually works on a five-year-old.
- Algorithm
- A fixed sequence of steps for completing a task. A recipe is an algorithm; so is long division. Nothing about the word implies intelligence.
- Say it to a child: "It is a list of steps, like the instructions for making a sandwich."
- Model
- The file that comes out of training — a very large set of numbers capturing patterns found in the data. When people say "the AI", they usually mean the model.
- Say it to a child: "It is the part that did all the practising, saved into a file."
- Training data
- The examples a model was shown while learning. Its strengths and its blind spots both come directly from here.
- Say it to a child: "It is the pile of examples we showed the computer so it could learn."
- Token
- The chunk a model actually reads. Roughly a short word or part of a longer one — "unhappiness" might arrive as three tokens.
- Say it to a child: "Computers chop words into little pieces before reading them."
- Large language model (LLM)
- A model trained to predict the next token across an enormous amount of text. Everything it produces — answers, poems, code — comes out of that single prediction step, repeated.
- Say it to a child: "It is a really good guesser that has read a huge number of sentences."
- Prompt
- The text you give a model. It is not a command so much as a starting point the model continues from — which is why phrasing changes the answer so much.
- Say it to a child: "It is what you ask the robot."
- Hallucination
- A confident, fluent, entirely invented answer. Not a bug being fixed soon — a direct consequence of a system that predicts plausible text rather than retrieving facts.
- Say it to a child: "Sometimes the robot daydreams and says something that is not true, without meaning to."
- Bias
- Systematic unfairness that a model inherits from gaps or skews in its training data. It is a data problem far more often than a code problem.
- Say it to a child: "If we only show the robot black cats, it will not believe an orange cat is a cat."
- Machine learning
- Building a system by showing it examples rather than writing rules for it. Nobody programmed "what a cat looks like" — the model worked it out from photographs.
- Say it to a child: "Instead of telling the computer the rules, we show it lots of examples."
- Reinforcement learning
- Learning by trial and reward. The system tries something, receives a score, and adjusts — the same loop as training a puppy with treats.
- Say it to a child: "The computer keeps trying, and we say 'yes, warmer!' when it gets closer."
- Computer vision
- Getting meaning out of images. The model never sees a picture as we do — only a grid of numbers, one per pixel.
- Say it to a child: "To a computer, a photo is just thousands of tiny coloured dots."
- AI agent
- A model given tools and a goal, allowed to take several steps on its own — look something up, use the result, decide what to do next.
- Say it to a child: "A helper that plans a job and does it one step at a time."
- Human in the loop
- A design where the machine proposes and a person decides. The standard safeguard wherever a wrong answer would actually matter.
- Say it to a child: "The computer helps, but a person always makes the final choice."
- On-device
- Computation that happens in the browser or on the phone itself, with nothing sent to a server. Nothing to intercept, store, or leak.
- Say it to a child: "Everything happens right here on our own computer. Nothing gets sent away."
13 Classroom-Ready AI Lesson Plans 🎒
Each module pairs a kinesthetic unplugged activity with a Mile Coder interactive simulation.
The Human Robot: Algorithms, Sequencing & Step-by-Step Instructions
Learning Goal: Understand that computers cannot think for themselves; they must follow exact, unambiguous step-by-step instructions called algorithms.
- • Toy block or stuffed animal
- • A blindfold or closed eyes
- • Floor space
Open Mission 4 on Mile Coder. Watch how Agent Miles looks at the obstacle, inspects its backpack, selects the key or bridge, and tries it step by step.
- Choose one person to be the "Robot" (who must freeze and only move when commanded).
- The children are the "Programmers". Their goal is to direct the robot to walk across the room and pick up a teddy bear.
- The programmers can only use 4 commands: "Step forward 1 foot", "Turn 90° right", "Turn 90° left", and "Reach hand down".
- If the programmers say "Go get the bear!", the robot tilts its head and says: "ERROR: Command too vague! What is the first step?"
- Celebrate when the sequence succeeds! Discuss why computers need exact steps.
- • “What happened when you gave a command that was too big or not specific?”
- • “Why do computers need us to break big problems into tiny steps?”
- • CSTA 1A-AP-08 — "Model daily processes by creating and following algorithms (sets of step-by-step instructions) to complete tasks." Met by programming a human robot with four fixed commands.
- • Australian Curriculum AC9TDI2P02 — "Follow and describe algorithms involving sequencing, comparison operations (branching) and iteration." Met at the sequencing level only.
The Prediction Machine: How Language Models Guess Words from Context
Learning Goal: Demystify Generative AI and LLMs: discover that language models do not understand meaning, but predict the most likely next word using patterns.
- • Whiteboard or paper
- • Familiar sentence strips or nursery rhymes
Have children play The Guessing Game on Mile Coder. Watch the robot suggest words one by one. Then use Word Chopper to see how sentences are chopped into tokens.
- Gather students in a circle. Write an incomplete sentence on the board: "I ate a slice of cheesy warm ______."
- Ask students to shout out what word comes next (Pizza! Bread! Pie!). Ask: How did your brain know it wasn’t "Bicycle"?
- Write another: "The cat chased the little gray ______." (Mouse!).
- Explain: A computer does this exact same game, but it counted millions of books to know that "pizza" is 95% likely after "cheesy warm".
- Try a silly one: "The spaceship landed on a giant scoop of ______." (Ice cream!). Notice how humans can imagine brand new things!
- • “Does the robot know what a dog feels like, or does it just know the word "dog"?”
- • “What happens when the robot guesses a word that is silly or unexpected?”
- • CSTA 1A-DA-06 — "Collect and present the same data in various visual formats." Met by tallying on the board which words the class predicted, then comparing.
- • UK National Curriculum, Computing KS1 — "Recognise common uses of information technology beyond school." Met by naming where next-word prediction already appears in their lives.
Training the Robot Puppy: Machine Learning, Rewards & Feedback Loops
Learning Goal: Understand that AI learns from examples and feedback (Reinforcement Learning) rather than pre-written rules.
- • Stickers or paper star tokens
- • A pretend "puppy" or bell
Children tap "Give Biscuit" 🍖 when the puppy rolls over or sits. Watch the puppy’s confidence meter climb until it masters the trick.
- One child is the "Learning Puppy" and steps outside the classroom for 10 seconds.
- The class chooses a secret goal (e.g., touch the yellow chair in the corner).
- The puppy returns. Whenever they take a step closer to the yellow chair, the class claps enthusiastically (positive reward / "treat!").
- If they step away, the class stops clapping and stays quiet.
- Within 30 seconds, the puppy finds the yellow chair through feedback alone, without anyone speaking words!
- Discuss: This is how AI robots learn to walk and play chess—through trials and reward stars!
- • “How did the puppy know what to do without being told the exact rule?”
- • “What happens if we give treats for the wrong trick by mistake?”
- • CSTA 1A-AP-10 — "Develop programs with sequences and simple loops, to express ideas or address a problem." Met at the sequence-and-loop level through the repeated feedback round.
- • AI4K12 Big Idea 3 (Learning) — "Computers can learn from data." Met by improving behaviour purely from reward signals, with no rule ever stated.
Robot Eyes & The Orange Cat: Computer Vision, Training Data & Algorithmic Bias
Learning Goal: Discover that an AI can only recognize what it has seen before. If the training data is missing something, the robot makes mistakes.
- • Toy animals of different colors
- • Drawing paper and crayons
Explore the pixel grid in Mission 8, then test the cat detector in Mission 9 when new colors are introduced.
- Show children 5 pictures of cats, but every single picture is a black cat.
- Tell the class: "You are a robot trained on these 5 photos. You have learned that cats have pointy ears, whiskers, and black fur."
- Now hold up a picture of a ginger/orange tabby cat or a white cat. Ask the robot: "Is this a cat?"
- The robot will say: "Error! It is orange, so it cannot be a cat!"
- Ask the children: What went wrong? Did the robot break, or was the photo album missing orange cats?
- Teach the big idea: To make AI fair and smart, humans must give it examples of all colors, shapes, and kinds of people and things.
- • “Why is it important to teach computers with lots of different examples?”
- • “Who is responsible when an AI makes an unfair mistake?”
- • AI4K12 Big Idea 5 (Societal Impact) — "AI can impact society in both positive and negative ways." Met by discussing who is harmed when training data is incomplete.
- • CSTA 1A-IC-16 — "Compare how people live and work before and after the implementation or adoption of new computing technology." Met in the closing discussion about who checks the robot's work.
Say What You Mean: Prompting, Precise Language & Why Wording Changes the Answer
Learning Goal: Discover that an AI answers the words you actually gave it, not the idea in your head — so clearer, more specific asks produce better results.
- • Paper and pencils for every child
- • A simple picture the class has not seen
- • A folder or book to hide it behind
Open Mission 17 and let children build a prompt from the colour, shape and extra word chips. Send a vague prompt first and watch several robots match, then add the missing words until only one robot is left.
- Pair the children up and sit them back to back. One is the Describer, one is the Drawer.
- Give the Describer a simple picture — a house with two windows, a tree on the left, three clouds — and hide it from the Drawer.
- The Describer may only use words. No pointing, no peeking, no showing.
- After two minutes, compare the drawing to the original. The gap between them is always funny, and always instructive.
- Now swap roles and run it again — but first, let the class agree on three rules for describing well (say where things are, say how big, say how many).
- Watch the second round come out dramatically better. Name what changed: not the drawer, the instructions.
- • “Did the drawer make a mistake, or did the describer leave something out?”
- • “What is one word you could add to a question to make it much clearer?”
- • “If the robot gives you an answer you did not want, whose turn is it to try again?”
- • CSTA 1A-AP-11 — "Decompose (break down) the steps needed to solve a problem into a precise sequence of instructions." Met by rebuilding a vague instruction into a specific one.
- • AI4K12 Big Idea 2 (Representation & Reasoning) — computers reason over the representation they are given, not over intent.
Stop, Check, Ask: Critical Thinking, Spotting Confident Mistakes & Knowing When to Fetch a Grown-Up
Learning Goal: Build one automatic, rehearsed routine for the moment an AI says something wrong, strange, or upsetting — and learn that a confident voice is not the same as a correct answer.
- • Three large signs: STOP ✋, CHECK 🔍, ASK 🧑
- • Sticky notes
- • A postcard and a sealed envelope
- • True and silly statements written on cards
Play Mission 18 together. Every answer arrives with the same 100% sure meter, so pause after each and ask the class how they could check whether it was really true.
- Read a few simple claims — some true, some silly — in a flat, completely certain voice. Ask: did I sound sure? (Very!) Does sounding sure make it true? (No!) Sort the cards into TRUE or MADE-UP and ask how the children knew.
- Hold up a postcard and a sealed envelope. Ask: which one can the postman read? Establish that typing to a robot is a postcard, not an envelope.
- Together, sort sticky notes into KEEP PRIVATE or FINE TO TYPE: my favourite dinosaur, my home address, a maths question, my school's name, a joke I made up, my password.
- Land the rule in the children's own words: names, places and passwords stay with us.
- Put the three signs — STOP, CHECK, ASK — on the wall. Read a scenario aloud and have the class point at the sign they would use first.
- Scenarios: the robot says something you know is wrong; the robot asks where you live; the robot says something that makes your tummy feel funny; the robot gives you an answer for homework.
- Finish with the line, said together: "The robot is a tool. A grown-up is a person. When something feels wrong, I go to the person."
- • “The robot sounded completely sure. Did that make it right?”
- • “What are three things we never type to a robot?”
- • “The robot said something wrong. Is that the robot being naughty, or the robot guessing?”
- • “Who is the person you would go and find?”
- • CSTA 1A-NI-04 — "Explain what passwords are and why we use them, and use strong passwords to protect devices and information from unauthorized access." Met through the private-versus-shareable sort.
- • AI4K12 Big Idea 5 (Societal Impact) — "AI can impact society in both positive and negative ways." Met by rehearsing the child's own response to a bad output.
Words Live Next Door: Word Maps, Meaning & Why Similar Words Sit Together
Learning Goal: Understand that an AI does not store words like a dictionary — it stores them by meaning, so "dog" and "puppy" live closer together than "dog" and "rocket".
- • Blank paper with one word drawn in the middle
- • Pencils or crayons
- • A small set of toy animals or food pictures
Have children pick a word in the Word Map mission, guess its three nearest neighbours, then press Check My Map to compare themselves with the robot’s ranking.
- Draw a big house in the middle of a page and write a common word inside it — DOG, CAKE or STAR.
- Ask the class: who are DOG’s neighbours — the words that mean almost the same thing? Collect answers: puppy, cat, bunny, pet, fur.
- Add one neighbour per house drawn next to the first. The closer the meaning, the closer the house.
- Then add far-away words — rocket, dinner, Monday — and ask the class to draw them across the street or out of town.
- Repeat with CAKE (cookie, candy, honey) and STAR (moon, rocket, sun). Praise any defended answer: the point is the shared idea.
- • “What makes "puppy" a better neighbour for "dog" than "rocket" is?”
- • “If the robot had only ever seen cooking words, where would it put "bowl"?”
- • “How is a robot’s word map different from a dictionary on a shelf?”
- • AI4K12 Big Idea 2 (Representation & Reasoning) — "AI systems represent knowledge, and use that representation to reason." Met by exploring a spatial meaning representation.
- • Australian Curriculum AC9EFLY01 — reading and viewing: identifying that words carry shared, related meanings. Met by building a meaning neighbourhood.
The Guess-It Machine: Decision Trees & Playing 20 Questions
Learning Goal: Discover that classification can be done faster (and taught to a robot) by asking yes/no questions that slice the possibilities in half.
- • A paper copy of the Guess-It Tree worksheet
- • Animal flashcards or the zoo box from the sheet
- • No batteries!
In the digital version, watch the "still hiding" row shrink from 8 to 4 to 2 to 1 as the class answers. Pause before the last answer every round: what is left?
- Choose a child to be "IT" who secretly picks an animal from the zoo box.
- Instead of guessing animals one by one, the class can only ask questions that split the list roughly in half — "does it live in water?", never "is it a fox?".
- Run a round using the printed tree as the question list. Count how many questions it takes to find the animal.
- Now try the slow way: guess one animal at a time with no questions. Count how many guesses it would take to be sure.
- Compare the two counts and land the big idea: every good question threw half the animals away.
- • “Why was "does it live in water?" such a good first question?”
- • “What would happen if the first question was "is it a fox?" instead?”
- • “Can you invent a tree for sorting fruit (2–3 questions) at dinner tonight?”
- • CSTA 1A-DA-06 — "Collect and present the same data in various visual formats." Met by recording and comparing the shrinking counts 8, 4, 2, 1.
- • AI4K12 Big Idea 1 (Perception) & Big Idea 2 (Representation & Reasoning) — Met by reasoning with attributes and building a classification structure.
Robot Ears: Speech Recognition, Listening & Sound as Numbers
Learning Goal: Understand how a robot "hears": sound becomes numbers, is chopped into tiny pieces, and is matched against sounds the robot has practised — and that the same trick is how the site talks back.
- • A cardboard "phone" or a pair of paper cups on a string
- • No special tech required
Have children close their eyes, press the play button, and point at the word the robot said. Notice the wiggly bars — that is the sound being turned into numbers.
- Play a classic whisper chain with one sentence — "Robot ears love wiggly air!" — and enjoy how garbled the end is.
- Then play the paper-cup phone: one child whispers a word into the cup, a partner guesses it across the string.
- Ask: what travelled from one cup to the other? Air wiggled! A microphone does the same, but turns the wiggle into numbers.
- Have a child draw the shake-lines (the "wiggle picture") of a loud word and a quiet word, then a loud quiet word.
- Finish: robots chop your wiggly air into tiny pieces, check each piece against sounds they have practised, and say the word they think fits.
- • “What really travelled between the two paper cups?”
- • “Why is a quiet whisper harder for a robot than a loud shout?”
- • “What is the difference between the robot talking and the robot listening?”
- • CSTA 1A-CS-02 — "Select and operate appropriate software to perform a variety of tasks." Met by operating on-device speech through a browser.
- • Australian Curriculum AC9S2U02 — exploring how sound is produced by vibrations. Met by the paper-cup phone and the wiggly-air discussion.
Safe or Not Safe?: Privacy & Knowing What Never Gets Typed
Learning Goal: Make the privacy pause automatic: some things are fine to share, some only with a grown-up, and names, places, photos and passwords are never typed to a robot, anywhere.
- • The Safe to Share? worksheet
- • Scissors and glue or sticky notes
- • Three coloured hoops or circles drawn on the floor
Play the mission as a group quiz on the big screen. Pause after each answer and let the class explain the why before pressing on.
- Draw three big circles on the floor: 🟢 Safe to share, 🟡 Ask a grown-up, 🔴 Never tell a robot.
- Hand each child one card from the worksheet (or a sticky note) and read it aloud.
- One child at a time, the child decides which hoop the card belongs in and steps in with it.
- The class gets one gentle challenge after each place: "Could a stranger use that to find them? Is it someone else's secret?"
- End in a circle and repeat the rule together: names, places and passwords stay with us.
- Give the class the winning test: "Would I be happy if the whole playground read this card?"
- • “What are the three things we never type anywhere?”
- • “Why does "my best friend’s name" belong with a grown-up's blessing?”
- • “A robot asks you a question you don’t want to answer. What can you do?”
- • CSTA 1A-NI-04 — "Explain what passwords are and why we use them." Met by sorting passwords and codes into the red hoop.
- • AI4K12 Big Idea 5 (Societal Impact) — "AI can impact society in both positive and negative ways." Met by rehearsing private-vs-shareable boundaries in the child's own words.
The Attention Web: How Transformers Connect Words Across Sentences
Learning Goal: Demystify the Transformer architecture by demonstrating how AI uses self-attention to process whole sentences simultaneously and connect related words.
- • A ball of colorful yarn or string
- • Word cards pinned to student shirts (Elephant, Sprayed, Car, Because, It, Dirty)
- • Flashlight / spotlight
Open the game on the classroom screen. Let students tap words to reveal neon energy beams and solve pronoun and context riddles.
- Assign 6 students word cards to form the sentence: "The elephant sprayed the car because it was dirty."
- Give the child wearing "It" the ball of yarn. Ask: "Who or what is dirty?"
- "It" unrolls the yarn and tosses the ball directly to "Car" (holding the tight string), and then to "Dirty".
- Point out: the string connects "It" to "Car" across the room instantly, without touching the words in between!
- Contrast this with the "Snail Walk": have another student slowly step from word to word, demonstrating how older models processed text sequentially.
- Repeat with a new sentence: "Maya gave Leo a cupcake because she was generous."
- • “Why is looking at all words together faster than reading one by one like a snail?”
- • “How did the yarn show who was dirty in the sentence?”
- • “When a word has two meanings (like a river bank vs a coin bank), how do other words help the robot pick the right one?”
- • CSTA 1A-AP-09 — "Model the way a computer processes natural language."
- • AI4K12 Big Idea 3 (Learning) — "Computers can learn from data and contextual patterns."
Code the Human Robot: Algorithmic Thinking & Writing AI Recipes
Learning Goal: Understand that AI is software written by humans through triggers (WHEN), conditional checks (IF), and actions (THEN).
- • Large index cards labeled WHEN (Triggers), IF (Conditions), THEN (Actions)
- • Robot headband or foil hat for the designated "robot" student
- • Whiteboard and markers
Students build and run their own mini JavaScript scripts in the Junior AI Code Lab to trigger jokes, dances, and high-fives.
- One student or the teacher wears the Robot Headband and stands frozen like a powered-down robot.
- Students work in pairs to assemble three recipe cards: WHEN (e.g. Someone claps twice), IF (e.g. Robot has hands free), THEN (e.g. Spin around and beep).
- The "Programmer" student holds up the code card. The class executes the trigger (clapping twice).
- The "Robot" checks the condition and performs the exact action!
- Introduce a "bug": what if the trigger is missing? The robot stays frozen! Show that computers need explicit instructions.
- Reinforce: robots are not magic; people write the code recipes that make them work.
- • “Who was really in charge—the robot, or the programmer who wrote the card?”
- • “What happens if a recipe leaves out an important step?”
- • “Why do coders call step-by-step instructions an algorithm?”
- • CSTA 1A-AP-08 — "Model daily processes by creating and following algorithms."
- • CSTA 1A-AP-10 — "Develop programs with sequences and simple loops."
AI Safety Superhero Squad: The 4 Golden Rules for Safe Digital Citizenship
Learning Goal: Instill four protective digital habits: keeping private data secure, recognizing AI hallucinations, maintaining academic honesty, and reporting distressing outputs.
- • Printable AI Safety Superhero Pledge sheets
- • Superhero badges or stickers
- • Chart paper with the 4 Golden Rules
Guide learners through the four interactive dilemma trials to power up their virtual superhero suit and print their certificate.
- Introduce the four superhero tools: The Privacy Vault (Shield), Truth Goggles (Magnifier), Brain Captain Hat (Compass), and Red Alert Bell.
- Read four classroom dilemma cards aloud (e.g., a chatbot asking for a password, an AI writing fake dinosaur facts, copying a poem for homework, seeing a scary response).
- Students strike a Superhero Pose with their arms if the move is safe, or cross their arms in an "X" if it is risky!
- Discuss the safest solution for each card and have students practice the sentence: "I will check with my teacher first!"
- Students sign their Superhero Pledge certificates and commit to the 4 Golden Rules.
- • “What are three things that should always stay locked in your private vault?”
- • “Why does a confident voice from a robot not guarantee that a fact is true?”
- • “Who is on your safety team when a screen makes you feel uncomfortable?”
- • CSTA 1A-NI-04 — "Identify safe behaviors and private data online."
- • AI4K12 Big Idea 5 — "Societal impacts, ethics, and safety considerations of AI."
The AI Puzzle Arcade 🕹️
Five sets of quick puzzles, each one hiding a real idea from computer science. Every puzzle explains itself after you answer — so a wrong guess teaches as much as a right one.
Next-token prediction. A language model does not know what it is saying — it continues the pattern that is most likely, exactly like this puzzle.
How to play: Look at the row of pictures. What should go in the empty box? Pick the answer, then say out loud how you knew.
What comes next?
Junior AI Explorer Certificate
Print one, or paste a whole class list and print the lot in a single job. Leave a name blank to get a ruled line to write on by hand.
Certificate of AI Exploration
This certifies with honors that
has successfully investigated the fundamentals of artificial intelligence, including next-word prediction, machine training, computer vision, and human superpowers.
Pick a sheet, then print. Only the sheet you are looking at goes to the printer.
Program Robot Miles: The Star Maze 🧭
Instructions: Robot Miles needs to reach the battery charging dock ⚡! In each code box below, draw the arrow that moves him one square — ⬆️ up, ⬇️ down, ⬅️ left, or ➡️ right. Do not land on a water puddle 💧.
AI Conversation Starter Cards 🎲
Twenty thoughtful, non-intimidating questions to spark wonder and critical thinking during family meals, car rides, or classroom morning meetings.
“If you had a robot helper at home, what is ONE chore you would ask it to do?”
Great for sparking conversation about automation. Follow up: Would you trust the robot to clean your favorite fragile toy?
School IT & Privacy Blueprint 🛡️
Here is exactly what this site does and does not do, so your IT team can check it against your own policy rather than take our word for it.
Everything runs in the browser
Every game, puzzle and AI simulation is plain JavaScript running in the tab. The simulated language models, Markov chains and classifiers compute on the child's own device. No activity sends a request to an AI provider.
No accounts, no analytics
There is no sign-up, and children are never asked for a name, email, password or any personal detail. The site loads no analytics, advertising or tracking scripts of any kind. Badge progress is written to localStorage on that one device and is gone when browser data is cleared.
No microphone, no camera
Read-aloud uses the browser's built-in SpeechSynthesis voice. The site never requests microphone or camera access, records nothing, and uploads no audio or images.
Privacy Act, COPPA and GDPR
We're an Australian operator and we apply the Australian Privacy Principles voluntarily, even though our size means the Privacy Act 1988 doesn't currently bind us. Because we collect no personal information from children, there is also nothing requiring verifiable parental consent under COPPA, and no personal data processed under GDPR Article 8. We can't certify your deployment for you — that depends on your school or district's own policy — but we're glad to answer your reviewer's questions in writing.
What does leave the device
Being straight with you: loading any website means your browser requests the page from a server, and our host records standard web-server request logs (IP address, timestamp, page requested) the same way every website does. That is the whole list. Nothing a child types or clicks inside an activity is ever transmitted.
Play the 16 AI Missions with Your Child or Class
Step-by-step interactive games explaining prediction, tokens, agents, and robot training.