AI Activity 1: AI Art Creation
Benefit: Teaches AI prompt design, creativity, and visual inference for machine learning.
Tool options: DALL·E, Artbreeder, Midjourney (child-appropriate settings)
How to start:
Ask your child: “Describe a wild scene in three words (e.g. ‘unicorn skateboard’).”
Enter that into DALL·E (or your chosen generator).
Review the image together.
Tweak: change colors, change subject, add mood (e.g. “sunset unicorn skateboard”).
Try this 5–10 minute challenge: Generate an image with 1 noun + 1 adjective. Then AI generates a modified version by changing one word (adjective or noun). Compare results.
Pro tip: Encourage your child to think carefully about descriptive words (adjectives, lighting, textures). That kind of AI prompt refinement is where they learn nuance.
AI Activity 2: AI Storytelling / Interactive Narratives
Benefit: Develops narrative sense, branching logic, and AI prompt design.
Tool options: ChatGPT, GPT-4, AI Dungeon (kid-safe mode)
How to do it:
Start a story AI prompt: “You discover a secret door in your school library. What happens next?”
Ask the AI Art to pause at decision points: “Do you go left or right?”
Your child picks, and AI continues.
After two or three branches, pause. Ask: “If I had asked differently, would it change the path in machine learning?”
Mini exercise (10 min): Let your child write two opening sentences. Feed both separately into ChatGPT and compare how the story diverges.
This helps them see how small AI prompt changes lead to very different narratives.
AI Activity 3: AI-Powered Games & Quizzes
Benefit: Shows adaptive learning, gamified AI feedback, and makes comprehension fun.
Examples: Google Experiments’ Quick, Draw!, adaptive quiz bots
How to play for machine learning:
Try Quick, Draw! where you sketch and the AI guesses, you see what patterns the AI “knows.”
Or use AI quiz tools where the next question depends on your answer (e.g., harder vs easier).
Exercise: Let your child propose a quiz question in a subject they like (math, geography). Use ChatGPT to generate two follow-up questions (one harder, one easier). Then test with them and see which they pick, and why.
Kids see how AI Art or adaptive systems respond to strengths and weaknesses.
AI Activity 4: Building a Simple Chatbot
Benefit: Basic introduction to NLP, logic, and conversation design.
Tool options: Chatbot.com, Tars, Dialogflow, or Machine Learning for Kids + Scratch
How to build:
Choose a topic (e.g. “Homework Helper” or “Joke Bot”).
Define 5 user intents (e.g. greeting, ask for joke, ask for help, say goodbye).
Write simple responses.
Use Dialogflow or Tars to map user input to intents.
Test with your child: “Tell me a joke,” “Help me with science,” etc.
Quick task (15 min): Build a “Joke Bot” with 3 intents: greeting, joke, farewell. Test it with your child, then refine responses.
This project gives a peek into how conversational AI Art works behind the scenes.
AI Activity 5: AI Music Composition
Benefit: Teaches mood, rhythm, generative art in sound.
Tools: Amper Music, AIVA, Jukedeck, Jukebox
How to begin:
Let your child pick a mood (e.g. “mysterious,” “happy”), instruments (piano, strings), tempo.
Hit “Generate.”
Listen together.
Tweak one parameter (tempo, instrument) and regenerate.
Mini challenge (10 min): Generate two versions of a song by changing one instrument or mood—then ask your child which they prefer and why.
It’s magical how AI Art can turn feelings into melodies. And your child learns that even music follows patterns it can learn.
AI Activity 6: Voice Assistant Challenges
Benefit: Exposes kids to voice interface logic, conditional commands, audio understanding.
Tools: Alexa Skills Kit, Siri shortcuts, Google Assistant, or simple command bots
Creative ideas:
Create a scavenger hunt: “Alexa, give me the next riddle/clue.”
Ask trivia: “Alexa, what’s the capital of Mongolia?”
Let your child design voice commands for actions (e.g. “Alexa, tell me a joke about cats”).
Exercise: Together, write 3 voice commands and test them. Then tweak phrasing to make the AI Art understand better (e.g., add “please,” reorder words).
This helps them see speech is just another kind of AI prompt.
AI Activity 7: AI Training / Object Recognition
Benefit: Introduces training sets, classification, model accuracy.
Tool: Google Teachable Machine learning
How to do it:
Go to Teachable Machine Learning → “Image Project.”
Upload ~20 pictures of two categories (e.g. dog vs cat, toy car vs truck).
Train the model.
Test with new images—see which ones it misclassifies.
Add more training data, retrain, test again.
Quick trial (10–15 min): Train a model to distinguish your hand vs a pen. Then test with odd angles or backgrounds.
You get immediate feedback on model mistakes—and you can iteratively improve.
AI Activity 8: Interactive Coding Projects (AI Extensions)
Benefit: Bridges block code with AI Art logic.
Platforms: Scratch (AI / ML extensions), Code.org, Tynker
What to try:
Use Scratch’s experimental Face Sensing or ML extensions to detect facial expressions, trigger sprites, etc. create-learn.us
Create a sprite that reacts when you smile or frown.
Or build a mini-game where AI activity adjusts difficulty dynamically.
Mini challenge (15 min): Build a sprite that cheers when you clap (via microphone input).
This AI activity blends visual coding and AI Art in a playful way—kids see cause-and-effect.
AI Activity 9: AI Ethics / Bias Experiment (Bonus)
Benefit: Helps kids understand AI’s limits, bias, and fairness.
How to do it:
Pick a simple AI prompt (e.g. “Describe a teacher”).
Query AI Art multiple times, see if descriptions shift in gender or descriptors.
Discuss: Why did it pick those words? Are they fair?
Quick discussion (10 min): Ask your child: “If I change one word (e.g. ‘teacher’ to ‘nurse’), does the description change? Why?”
This opens the door to critical thinking about AI—not blind trust.
AI Activity 10 (Plus): Prompt Remix Game
Benefit: Strengthens prompt-engineering intuition.
How to play:
Pick any earlier activity (e.g. art or storytelling).
Rewrite your prompt with a slight change.
Compare results side by side.
Rate which one “feels better” and why.
It’s simple but powerful: kids begin with AI in machine learning that how you ask matters as much as what you ask.
Assessment & Progress Tracking
Kids learn best when they can see growth—not just results. Here are steps you can use:
Project journal: After each AI activity, let the child jot (or voice-record) what worked, what surprised them, what they’d change.
Progress portfolio: Save each version of an AI project (image versions, chatbot iterations, etc.).
Mini reflections: After 3 or 4 activities, pause and ask: “What pattern do you see in how AI responded?”
Choose a capstone: Let them pick one project to evolve further over days or weeks.
Real-life example: My nephew spent two afternoons training image recognizers (dog vs toy) and noticed the model struggled when the background changed. He then added ~30 more images with varied backgrounds to the training set—and saw accuracy jump from ~60% to ~90%. That was a “wow” moment for him: he saw mistakes, iterated, and improved.
