10 Hands-On AI Projects Your Kids Can Build This Year

Want to know the secret to making AI education stick? Stop talking about AI and start building with it.

Theory is important, but there's no substitute for the "aha!" moment when a child trains their first machine learning model and watches it actually work. That's when AI transforms from an abstract concept to something tangible, exciting, and full of creative possibilities.

This guide presents 10 hands-on AI projects specifically designed for kids aged 9-13. Each project includes everything you need: difficulty level, required materials, step-by-step instructions, learning outcomes, and ideas for extending the project further.

Whether your child has never coded before or already has programming experience, there's a project here that will challenge, engage, and inspire them.

How to Use This Guide

Difficulty Levels:

• 🟢 Beginner: No prior experience needed
• 🟡 Intermediate: Some coding or tech experience helpful
• 🔴 Advanced: Requires coding knowledge and patience

Time Estimates:

• ⏱️ Quick (30-60 minutes)
• ⏱️⏱️ Medium (1-3 hours)
• ⏱️⏱️⏱️ Long (Multiple sessions)

Materials Key:

• 💻 Computer/tablet required
• 📸 Webcam/camera needed
• 🎤 Microphone needed
• 🧱 Physical materials required
• 💰 Paid tools (most projects use free tools)

Let's dive in!

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Project 1: AI Ocean Cleanup Detective

Difficulty: 🟢 Beginner | Time: ⏱️ Quick | Materials: 💻

What You'll Build

Train an AI to distinguish between ocean creatures and trash, helping solve a real environmental problem through machine learning.

Why It's Great

Code.org's "AI for Oceans" uses real machine learning techniques (TensorFlow MobileNet and Support-Vector Machines) while being completely accessible to beginners. Kids see immediate results and understand how training data affects AI performance.

What You'll Learn

• How AI "learns" from examples
• Training data and classification
• Algorithmic bias (what happens if you only show certain types of fish?)
• Real-world AI applications for environmental issues

Step-by-Step Instructions

Step 1: Access the Project

• Go to code.org/oceans
• No account needed to start!
• Click "Begin"

Step 2: Meet Your AI Bot

• You'll meet "A.I." (the bot you're training)
• Your mission: Help A.I. learn to identify fish vs. trash in ocean images

Step 3: Provide Training Data

• The AI shows you images
• You label each one as "fish" or "not fish"
• Start with at least 20 examples of each category
• The more examples you provide, the better A.I. learns

Step 4: Test Your Model

• Let A.I. classify new images on its own
• Check accuracy—how many does it get right?
• If accuracy is low, provide more training examples

Step 5: Expand the Challenge

• Now classify underwater creatures: "fish" vs. "sea creatures" (like jellyfish, turtles)
• This teaches nuance—not everything in the ocean is fish or trash!

Step 6: Create Your Own Categories

• Choose your own labels for randomly generated fish images
• Train the AI with your custom categories
• Test how well it learned your classification system

Discussion Questions for Parents

• "Why do you think the AI made mistakes on some images?"
• "What would happen if we only showed the AI pictures of goldfish? Would it recognize a shark?"
• "How is this similar to how you learned to recognize different animals when you were younger?"

Extension Ideas

Easy Extensions:

• Document your AI's accuracy rate before and after additional training
• Create a guide: "How to Train an Ocean-Cleaning AI"
• Research: How is AI actually being used to help ocean cleanup?

Advanced Extensions:

• Research computer vision and how image classification works
• Investigate bias: What if training data only included tropical fish?
• Design your own classification problem (birds vs. planes vs. drones)

Real-World Connection

This isn't just a game! Organizations like The Ocean Cleanup use computer vision and AI to identify and track ocean pollution. Your child is learning the same concepts used by real environmental scientists.

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Project 2: Gesture-Controlled Game with Teachable Machine

Difficulty: 🟢 Beginner | Time: ⏱️⏱️ Medium | Materials: 💻 📸

What You'll Build

Create a game controlled by your hand movements—no controller needed! Wave your hand to make a character move, or create rock-paper-scissors that a computer can "see."

Why It's Great

Teachable Machine by Google makes training AI models incredibly visual and intuitive. Kids see their webcam feed, train the model by performing gestures, and get immediate feedback. The "magic" of AI becomes understandable.

What You'll Learn

• Image recognition and computer vision
• Training machine learning models
• How AI can recognize patterns
• Connecting AI to real applications

Required Accounts/Tools

• teachablemachine.withgoogle.com (free, no account needed)
• machinelearningforkids.co.uk/scratch (for integrating with Scratch)

Step-by-Step Instructions

Part 1: Train Your Gesture Recognition Model

Step 1: Set Up Teachable Machine

• Visit teachablemachine.withgoogle.com
• Click "Get Started"
• Choose "Image Project" → "Standard image model"

Step 2: Create Your Gesture Classes For a simple game, create 3-4 classes:

• Class 1: "Left" (hand on left side of screen)
• Class 2: "Right" (hand on right side of screen)
• Class 3: "Up" (hand raised high)
• Class 4: "Neutral" (no hand visible or hand at rest)

Step 3: Record Training Examples

• For each class, click "Hold to Record"
• Record 30-50 examples of each gesture
• Important: Vary your examples:
  • Move your hand slightly in different positions
  • Try different lighting
  • Use both hands
  • Have a parent or sibling try it too

Step 4: Train Your Model

• Click "Train Model"
• Wait 1-2 minutes while it learns
• Watch the accuracy metrics

Step 5: Test Your Model

• The Preview window shows real-time predictions
• Try each gesture—does it recognize them correctly?
• If accuracy is low, add more examples and retrain

Part 2: Connect to Scratch (Optional but Fun!)

Step 6: Export Your Model

• Click "Export Model"
• Choose "Tensorflow.js"
• Select "Upload (shareable link)"
• Click "Upload my model"
• Copy the link provided

Step 7: Set Up Machine Learning for Kids

• Go to machinelearningforkids.co.uk
• Click "Get Started" → "Try it now"
• Select "+ Add a new project"
• Choose "recognising images"
• Name your project (e.g., "Gesture Game")

Step 8: Import Your Teachable Machine Model

• In your ML for Kids project, click "Use a Python model"
• Paste your Teachable Machine link
• Test that it's working

Step 9: Create Your Game in Scratch

• Click "Make"
• Open Scratch 3 with your ML model loaded
• Basic game code:

When Green Flag clicked
Forever
  Set [gesture] to (recognize image from webcam)
  If [gesture] = "Left" then
    Change x by -10
  If [gesture] = "Right" then
    Change x by 10
  If [gesture] = "Up" then
    Change y by 10

Step 10: Enhance Your Game

• Add sprites (characters)
• Create obstacles to avoid
• Add scoring
• Make multiple levels
• Get creative!

Project Variations

Easy Variations:

• Rock-Paper-Scissors: Train AI to recognize hand shapes, play against it
• Traffic Controller: Different gestures mean stop, go, slow down
• Dance Simon Says: AI recognizes dance moves, challenges players to copy

Advanced Variations:

• Posture Detective: Train AI to recognize good vs. bad sitting posture
• Sign Language Alphabet: Teach AI to recognize sign language letters
• Emotion Recognition: Train AI to recognize facial expressions (happiness, surprise, etc.)

Troubleshooting Tips

"The AI isn't recognizing my gestures well!"

• Add more training examples (aim for 50+ per gesture)
• Ensure good lighting
• Make gestures more distinct from each other
• Include varied backgrounds in training data

"It works for me but not my sibling!"

• The AI learned YOUR hands specifically
• Add training data showing your sibling's hands too
• This teaches an important lesson about diverse training data!

Learning Reflection Questions

• "How many examples did you need before the AI learned your gestures?"
• "Did it work equally well for everyone? Why or why not?"
• "What made certain gestures harder for the AI to recognize?"
• "How could this technology help people? (Think accessibility, gaming, medicine)"

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Project 3: Build Your Own Chatbot Travel Guide

Difficulty: 🟡 Intermediate | Time: ⏱️⏱️ Medium | Materials: 💻

What You'll Build

Create a chatbot that shares fun facts about countries, answers travel questions, and maybe even recommends vacation destinations. It's like having a personal travel agent powered by AI!

Why It's Great

Chatbots teach natural language processing concepts while being endlessly customizable. Kids can make chatbots about ANY topic they're passionate about—travel, dinosaurs, sports, video games, space exploration.

What You'll Learn

• Natural language processing (NLP)
• Conversation design
• User experience thinking
• If-then logic and decision trees

Required Tools

• Scratch (scratch.mit.edu) - Free, account optional
• OR Chatbot building platforms like:
  • MIT App Inventor (free)
  • Dialogflow (free tier available, Google account needed)

Step-by-Step Instructions (Scratch Version)

Step 1: Plan Your Chatbot's Personality

Create a quick profile:

• Name: What's your chatbot called?
• Specialty: What does it know about? (Countries, cities, landmarks, travel tips)
• Personality: Friendly? Funny? Educational? Professional?
• Capabilities: What questions can it answer?

Example: "TravelBot teaches facts about all 7 continents, recommends destinations based on interests, and shares fun trivia!"

Step 2: Design the Conversation Flow

Map out possible conversations:

User: "Hi!"
Bot: "Hello! I'm TravelBot! Want to learn about countries around the world?"

User: "Yes" / "Sure" / "OK"
Bot: "Awesome! Which continent interests you? (Africa, Asia, Europe, North America, South America, Australia, Antarctica)"

User: "Asia"
Bot: "Great choice! Asia has 48 countries! Want to hear about:
   A) Largest country in Asia
   B) Most populated city
   C) Random fun fact"

... and so on

Step 3: Set Up Your Scratch Project

• Go to scratch.mit.edu
• Create a new project
• Choose or design a chatbot sprite (character)

Step 4: Program Greeting and Introduction

when green flag clicked
say [Hi! I'm TravelBot! 🌍] for (2) seconds
ask [What's your name?] and wait
set [username v] to (answer)
say (join [Nice to meet you, ] (username)) for (2) seconds
say [I can tell you about countries around the world!] for (2) seconds
ask [Which continent interests you? Type: Africa, Asia, Europe, North America, South America, Australia, or Antarctica] and wait

Step 5: Create Response Logic

if <(answer) = [Asia]> then
  ask [Cool! Want to learn about: (A) Japan, (B) China, (C) India, or (D) Thailand?] and wait
  if <(answer) = [A] or <(answer) = [Japan]>> then
    say [Japan is an island nation with 125 million people! 🗾] for (3) seconds
    say [Tokyo is the capital and largest city with 14 million people!] for (3) seconds
    say [Fun fact: Japan has more than 6,800 islands!] for (3) seconds
  end
end

Step 6: Add More Countries and Facts

• Create variables for different continents and countries
• Add lists of fun facts
• Use random selection to share different facts each time

Step 7: Implement "Smart" Features

Make your chatbot seem smarter:

// Recognizing variations
if <(answer) = [hi] or <(answer) = [hello] or <(answer) = [hey]>> then
  say [Hello there! Ready to explore the world?] for (2) seconds
end

// Handling unknown inputs
if <not <(answer) = [Asia] or (answer) = [Europe] or ...>> then
  say [Hmm, I didn't understand that. Try typing a continent name!] for (2) seconds
end

Step 8: Add Personality Elements

• Include emojis 🌍 ✈️ 🗺️
• Add jokes or fun facts randomly
• Program responses to show enthusiasm
• Include encouragement: "Great question!" "That's so interesting!" "Want to learn more?"

Extension Ideas

Content Extensions:

• Add famous landmarks for each country
• Include traditional foods
• Share basic phrases in different languages
• Add capital cities and populations
• Include climate and geography info

Technical Extensions:

• Add voice synthesis (text-to-speech)
• Include images of countries/landmarks
• Create a quiz mode
• Build a points/scoring system for correct answers
• Add a recommendation engine: "Based on your interests, you'd love..."

Creative Extensions:

• Make different characters for different continents
• Add background changes when discussing different regions
• Create mini-games about geography
• Build a virtual passport that stamps each country you learn about

Conversation Design Tips

Do:

• Keep responses short (2-3 sentences max)
• Offer clear choices when possible
• Acknowledge user input
• Provide ways to restart or go back
• Include helpful error messages

Don't:

• Create dead ends in conversation
• Make users type long, complex answers
• Assume users will spell perfectly
• Leave users confused about what to do next

Real-World Applications

"Did you know? Chatbots like yours power:

• Customer service on websites
• Virtual assistants (Siri, Alexa, Google Assistant)
• Educational tutors
• Healthcare appointment booking
• Food delivery apps"

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Project 4: Smart Art Gallery with AI Image Recognition

Difficulty: 🟡 Intermediate | Time: ⏱️⏱️⏱️ Long | Materials: 💻 📸

What You'll Build

Create an interactive art gallery where AI identifies objects in images or classifies artwork by style. Show it a photo and it tells you what's in it—like magic, but it's machine learning!

Why It's Great

This project combines creativity (art!) with technical skills (AI training), and kids create something they can actually demo to friends and family. Plus, it introduces computer vision concepts used in everything from smartphone cameras to self-driving cars.

What You'll Learn

• Computer vision basics
• Image classification
• Dataset creation
• Model training and testing
• Presentation skills

Required Tools

• Teachable Machine (teachablemachine.withgoogle.com) - Free
• OR Scratch with Machine Learning for Kids
• Collection of images (can use drawings, photos, or online images)

Step-by-Step Instructions

Phase 1: Choose Your Gallery Theme

Pick one of these options (or create your own!):

Option A: Object Detector

• Classify everyday objects: books, toys, food, plants, electronics
• Take photos of items around your house
• Train AI to identify them

Option B: Art Style Classifier

• Classify art by style: realistic, abstract, cartoon, pixel art
• Use images from free art websites or create your own
• Train AI to recognize different artistic styles

Option C: Animal Classifier

• Focus on a category: dogs, cats, birds, or sea creatures
• Find diverse images of each type
• Train AI to distinguish between them

Example: We'll build an "Art Style Gallery"

Phase 2: Collect Your Dataset

Step 1: Gather Images for Each Category

For Art Style Gallery, collect 30-50 images of each style:

• Realistic Art: Photographs, photorealistic paintings
• Abstract Art: Non-representational, shapes and colors
• Cartoon Style: Comic books, animated art
• Pixel Art: Video game graphics, 8-bit style

Where to Find Images:

• Free art websites: Unsplash, Pixabay, WikiArt (with attribution)
• Draw your own examples!
• Photographs you take
• Public domain art collections

Step 2: Organize Your Dataset

• Create folders for each style
• Name files clearly: "realistic_01.jpg", "abstract_01.jpg"
• Ensure variety within each category

Phase 3: Train Your AI Model

Step 3: Set Up Teachable Machine

• Go to teachablemachine.withgoogle.com
• Choose "Image Project"
• Select "Standard image model"

Step 4: Create Your Classes

• Click "Add a class" for each art style
• Name them clearly: "Realistic", "Abstract", "Cartoon", "Pixel Art"

Step 5: Upload Training Images

• For each class, click "Upload"
• Select your 30-50 images
• Check that they're loading correctly

Step 6: Add Variety

• Use your webcam to add more examples
• Show printed images to the camera
• Display images on another screen
• This helps the AI generalize better

Step 7: Train Your Model

• Click "Train Model"
• Advanced Settings:
  • Epochs: 50 (how many times it reviews the data)
  • Batch size: 16
  • Learning rate: 0.001 (default)
• Training takes 2-5 minutes

Step 8: Test Performance

• Use the Preview panel
• Show different images to your webcam
• Check: Does it correctly identify the style?
• Look at confidence percentages (should be >80% for good predictions)

Phase 4: Build Your Interactive Gallery

Step 9: Export Your Model

• Click "Export Model"
• Choose "Tensorflow.js" for web use
• Upload and get your shareable link
• Save this link somewhere safe!

Step 10: Create Gallery Interface

Option A: Simple Web Interface (You'll need basic HTML/JavaScript knowledge or a parent's help)

Create a simple webpage:

<!DOCTYPE html>
<html>
<head>
  <title>My AI Art Gallery</title>
</head>
<body>
  <h1>🎨 AI Art Style Detector</h1>
  <p>Show me a piece of art and I'll identify its style!</p>
  <div>
    <video id="webcam" width="400" height="400"></video>
  </div>
  <div id="result">
    <p>Style: <span id="style">Loading...</span></p>
    <p>Confidence: <span id="confidence">--%</span></p>
  </div>
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
  <script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image"></script>
  <script>
    // Your Teachable Machine model URL
    const URL = "YOUR_MODEL_URL_HERE/";

    async function init() {
      const modelURL = URL + "model.json";
      const metadataURL = URL + "metadata.json";
      model = await tmImage.load(modelURL, metadataURL);

      // Setup webcam
      const flip = true;
      webcam = new tmImage.Webcam(400, 400, flip);
      await webcam.setup();
      await webcam.play();
      window.requestAnimationFrame(loop);
      document.getElementById("webcam").appendChild(webcam.canvas);
    }

    async function loop() {
      webcam.update();
      await predict();
      window.requestAnimationFrame(loop);
    }

    async function predict() {
      const prediction = await model.predict(webcam.canvas);
      const topPrediction = prediction.reduce((max, p) =>
        p.probability > max.probability ? p : max
      );

      document.getElementById("style").innerHTML = topPrediction.className;
      document.getElementById("confidence").innerHTML =
        (topPrediction.probability * 100).toFixed(1) + "%";
    }

    init();
  </script>
</body>
</html>

Option B: Scratch Integration

• Use Machine Learning for Kids (machinelearningforkids.co.uk)
• Import your model
• Create interactive Scratch project with:
  • Webcam input
  • Visual display of identified style
  • Counter tracking correct identifications
  • Gallery of example images

Phase 5: Create Your Gallery Experience

Step 11: Design the Presentation

• Welcome screen explaining the gallery
• Instructions: "Show artwork to the camera"
• Visual feedback (colors, animations) for each style
• Information cards about each art style
• "Try Again" or "Next Artwork" buttons

Step 12: Add Educational Elements

For each art style, create info cards:

Realistic Art

• "Looks like real life!"
• Famous examples: Mona Lisa, photographs
• Key features: Details, proportions, shading

Abstract Art

• "Shows ideas and feelings through shapes and colors"
• Famous artists: Kandinsky, Mondrian
• Key features: No recognizable objects, emotional expression

Cartoon Style

• "Simplified, exaggerated, and fun!"
• Examples: Comic books, animated movies
• Key features: Bold outlines, expressive faces, action lines

Pixel Art

• "Made from tiny colored squares!"
• Famous examples: Classic video games (Pac-Man, Mario)
• Key features: Blocky appearance, limited colors, nostalgic

Testing Your Gallery

Create a Testing Checklist:

• Does it correctly identify all 4 styles?
• Is confidence usually above 70%?
• Does it work with different lighting?
• Can others use it easily?
• Are instructions clear?
• Does it handle mistakes gracefully?

Improve Your Model: If accuracy is low:

• Add more diverse training images
• Ensure clear differences between categories
• Remove ambiguous images
• Retrain with adjusted settings

Exhibition Ideas

Host a Gallery Opening:

• Invite family and friends
• Demonstrate your AI gallery
• Let visitors test it with their own art
• Create printed guides about each style
• Award certificates: "Visited the AI Art Gallery!"

Create a Gallery Tour Video:

• Record yourself explaining the project
• Show the AI in action
• Explain how it works (in kid-friendly terms)
• Share challenges you overcame
• Post to family social media or school portfolio

Extensions and Challenges

Easy Extensions:

• Add sound effects for each style identification
• Create a scoring game: "How many styles can you show in 60 seconds?"
• Design badges or achievements
• Add a "surprise me" feature showing random art examples

Medium Extensions:

• Add more art styles (impressionism, surrealism, pop art)
• Create an "art history timeline" showing style evolution
• Build a quiz mode: show art, let users guess, reveal AI's prediction
• Track statistics: which style is most common?

Advanced Extensions:

• Train a model that identifies specific artists (Picasso vs. Van Gogh)
• Create a "style transfer" feature using AI art generation
• Build a recommendation system: "If you like Realistic, try Abstract!"
• Combine with AI art generation: classify AI-created art

Real-World Applications

"Computer vision like this powers:

• Google Lens (identify anything with your camera)
• Pinterest visual search
• Museum audio guides that recognize paintings
• Medical imaging analysis
• Quality control in manufacturing
• Wildlife camera trap identification"

Learning Reflection

Discussion Questions:

• "How does the AI 'see' art differently than humans do?"
• "What makes two art styles similar or different to the AI?"
• "Could AI ever appreciate art the way people do? Why or why not?"
• "How accurate does AI need to be for different real-world uses?"

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Project 5: Train an AI to Play Rock-Paper-Scissors

Difficulty: 🟢 Beginner | Time: ⏱️ Quick | Materials: 💻 📸

What You'll Build

Teach a computer to recognize your hand shapes and play rock-paper-scissors against you. It's the perfect introduction to AI training because everyone knows the rules!

What You'll Learn

• Image classification
• Real-time AI predictions
• How training data quality affects results
• Friendly human-AI competition

Quick Instructions

Step 1: Go to Teachable Machine (teachablemachine.withgoogle.com)

Step 2: Create 3 classes:

• Rock (fist)
• Paper (flat hand)
• Scissors (two fingers)

Step 3: Record 50 examples of each hand shape

• Vary hand position, angle, distance
• Use both hands
• Try different backgrounds

Step 4: Train your model (takes 1-2 minutes)

Step 5: Test it—throw different shapes, see if AI recognizes them

Step 6: Play a game!

• Make your throw
• AI predicts what you threw
• Determine winner (Rock beats Scissors, Scissors beats Paper, Paper beats Rock)
• Keep score!

Make It Better

• Add a timer (3...2...1...Shoot!)
• Create an AI opponent that "throws" randomly
• Track win/loss/tie statistics
• Add sound effects
• Make it best out of 5, 10, or 20 rounds

Extension: Can You Beat the AI?

Train the AI to recognize which throw you make most often—then try to trick it!

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Project 6: Build a Recommendation Bot

Difficulty: 🟡 Intermediate | Time: ⏱️⏱️ Medium | Materials: 💻

What You'll Build

Create an AI that recommends books, movies, games, or music based on what someone likes. It's like having your own Netflix or Spotify algorithm!

What You'll Learn

• Recommendation algorithms
• Pattern matching
• Database organization
• User preference tracking

How It Works

The Simple Version: "If you liked X, you'll probably like Y"

• Both are science fiction
• Both have adventure themes
• Both are appropriate for your age
• Other people who liked X also liked Y

Your AI Will:

1. Ask what the user already likes
2. Find common features in those choices
3. Suggest similar items
4. Learn from feedback (did they like the suggestion?)

Step-by-Step Instructions

Phase 1: Choose Your Recommendation Topic Pick something you know well:

• Books
• Movies/TV shows
• Video games
• Music artists/bands
• YouTube channels
• Sports teams
• Board games

Example: We'll build a "Book Recommendation Bot"

Phase 2: Create Your Database

Step 1: Build Your Collection List 20-30 items with key features:

Book Database:
1. Harry Potter - Fantasy, Magic, Adventure, Friendship, Ages 9-13
2. Percy Jackson - Mythology, Adventure, Humor, Ages 9-13
3. Wonder - Realistic Fiction, Friendship, Kindness, Ages 8-12
4. Holes - Mystery, Adventure, Friendship, Ages 9-12
5. The Giver - Dystopian, Thought-provoking, Ages 11+
... (continue for all books)

Step 2: Define Categories Organize by:

• Genre (Fantasy, Realistic Fiction, Mystery, etc.)
• Theme (Friendship, Adventure, Coming-of-age)
• Age appropriateness
• Reading level
• Mood (Funny, Serious, Inspiring, Scary)

Phase 3: Build Your Bot in Scratch

Step 3: Set Up Variables

// Variables
(username)
(favorite_book)
(genre_preference)
(mood_preference)
(recommendation)
(recommendation_reason)

Step 4: Create Lists for Your Database

[BookNames v] list: Harry Potter, Percy Jackson, Wonder...
[BookGenres v] list: Fantasy, Mythology, Realistic Fiction...
[BookThemes v] list: Magic-Adventure, Mythology-Humor, Kindness...
[BookAges v] list: 9-13, 9-13, 8-12...

Step 5: Program the Conversation

when green flag clicked
say [Hi! I'm BookBot, your reading recommendation assistant! 📚] for (2) seconds
ask [What's your name?] and wait
set [username v] to (answer)
say (join [Great to meet you, ] (username)) for (2) seconds

ask [What's a book you really enjoyed?] and wait
set [favorite_book v] to (answer)

ask [What did you like about it? (Type: adventure, funny, mysterious, friendship, magic, or other)] and wait
set [genre_preference v] to (answer)

ask [What mood are you in? (Type: excited, thoughtful, happy, or curious)] and wait
set [mood_preference v] to (answer)

say [Analyzing your preferences...] for (2) seconds
say [Finding the perfect book for you...] for (2) seconds

// Run recommendation algorithm (see next step)

Step 6: Build the Recommendation Logic

// Simple matching algorithm
if <(genre_preference) = [adventure]> then
  if <(mood_preference) = [excited]> then
    set [recommendation v] to [Percy Jackson]
    set [recommendation_reason v] to [action-packed mythology adventure]
  end
  if <(mood_preference) = [curious]> then
    set [recommendation v] to [Holes]
    set [recommendation_reason v] to [mysterious adventure with puzzles]
  end
end

if <(genre_preference) = [funny]> then
  if <(age) > [10]> then
    set [recommendation v] to [Diary of a Wimpy Kid]
    set [recommendation_reason v] to [hilarious everyday adventures]
  end
end

// Continue for all combinations...

// Present recommendation
say (join [I recommend: ] (recommendation)) for (3) seconds
say (join [Why? It's ] (recommendation_reason)) for (3) seconds
ask [Does this sound interesting? (yes/no)] and wait

if <(answer) = [yes]> then
  say [Awesome! Happy reading! Want another recommendation?] for (2) seconds
else
  say [No problem! Let me find something else...] for (2) seconds
  // Run algorithm again with different criteria
end

Step 7: Add Smart Features

"Other readers who liked X also liked Y"

// If they liked Harry Potter
if <(favorite_book) contains [Harry Potter]> then
  say [Fans of Harry Potter also love Percy Jackson and The Chronicles of Narnia!] for (3) seconds
end

Track successful recommendations:

// Keep a list of books they were interested in
when user says [yes]
add (recommendation) to [interested_books v]

Avoid repeating recommendations:

// Check if already recommended
if <[interested_books v] contains (recommendation)> then
  // Choose different book
end

Phase 4: Make It Smarter

Add More Factors:

• Reading level (easy, medium, challenging)
• Series vs. standalone books
• Book length (short, medium, long)
• Newer releases vs. classics

Create Better Matching:

• Count how many features match
• Rank recommendations by match quality
• Explain why each book is a good fit

Learn from Feedback:

if user liked recommendation then
  // Note which features matched
  // Prioritize those features next time
else
  // Try different approach
  // Avoid similar features
end

Extension Ideas

Easy Extensions:

• Add more books to your database
• Include cover images or descriptions
• Create multiple recommendation categories
• Add "surprise me" feature for random suggestions

Medium Extensions:

• Build a rating system (1-5 stars)
• Track all recommendations and feedback
• Create user profiles that remember preferences
• Make "opposite day" recommender (if you want happy, suggests serious)

Advanced Extensions:

• Implement actual machine learning using ML for Kids
• Create collaborative filtering (analyze patterns across many users)
• Build a web interface with your recommendations
• Train AI to write book descriptions or reviews

Real-World Applications

"Recommendation systems like yours power:

• Netflix movie suggestions
• Spotify playlists
• Amazon product recommendations
• YouTube video suggestions
• TikTok For You page
• Friend suggestions on social media"

Testing Your Recommendation Bot

Create Test Cases:

Test Case 1:

• User likes: Harry Potter
• Preferences: Magic, Adventure
• Should recommend: Percy Jackson, Chronicles of Narnia

Test Case 2:

• User likes: Diary of a Wimpy Kid
• Preferences: Funny, Easy reading
• Should recommend: Big Nate, Dork Diaries

Test Case 3:

• User likes: The Giver
• Preferences: Thought-provoking
• Should recommend: Fahrenheit 451, The Hunger Games

Invite Friends to Test:

• Do recommendations make sense?
• Are suggestions actually similar to preferences?
• Does the bot explain recommendations clearly?
• Can they find books they want to read?

---

Project 7: Create AI-Generated Art

Difficulty: 🟢 Beginner | Time: ⏱️ Quick | Materials: 💻

What You'll Build

Use AI art generation tools to create unique images from text descriptions. Then explore how AI "imagines" concepts and styles.

What You'll Learn

• Text-to-image AI
• Prompt engineering (how to describe what you want)
• AI creativity and limitations
• Artistic collaboration with AI

Recommended Tools (Free Options)

For Kids:

• Bing Image Creator (bing.com/create) - Free, powered by DALL-E
• Craiyon (craiyon.com) - Free, no account needed
• Google ImageFX (aitestkitchen.withgoogle.com) - Free with Google account

With Parent Help:

• Adobe Firefly (firefly.adobe.com) - Free tier available
• Canva AI (canva.com) - Free features included

Step-by-Step Instructions

Step 1: Choose Your AI Art Tool

• Visit one of the recommended platforms
• Create account if needed (get parent permission!)
• Explore the interface

Step 2: Learn Prompt Basics

Good prompts include:

• Subject: What you want to see
• Style: How it should look
• Details: Colors, mood, setting
• Specific instructions: Angle, lighting, focus

Examples:

❌ Bad: "dog" ✓ Better: "cute puppy playing in a garden" ✓✓ Best: "golden retriever puppy playing with a red ball in a sunny flower garden, photorealistic, happy mood"

❌ Bad: "space" ✓ Better: "astronaut in space" ✓✓ Best: "astronaut floating in space with Earth in background, stars everywhere, digital art style, inspiring feeling"

Step 3: Experiment with Styles

Try the same subject in different styles:

• "Robot playing guitar, cartoon style"
• "Robot playing guitar, photorealistic"
• "Robot playing guitar, watercolor painting"
• "Robot playing guitar, pixel art"
• "Robot playing guitar, pencil sketch"

Step 4: Create a Series

Project Ideas:

"My Dream Vacation" Series

• Create 5 images of imaginary vacation destinations
• Example prompts:
  • "Treehouse hotel in a rainbow forest"
  • "Underwater restaurant with fish swimming by windows"
  • "Castle made of ice cream on a candy land"

"Impossible Animals" Series

• Combine two animals
• Examples:
  • "Cat with butterfly wings"
  • "Elephant with peacock tail feathers"
  • "Dolphin with zebra stripes"

"Book Cover Creator"

• Design covers for your favorite books
• Or create covers for books you wish existed!

"Future Inventions"

• Imagine helpful inventions
• Examples:
  • "Robot that helps kids with homework"
  • "Flying skateboard"
  • "Translator device that understands animal language"

Step 5: Keep a Creation Journal

For each artwork, record:

• Your prompt (exactly what you typed)
• What you got vs. what you expected
• What you'd change next time
• Success rating (1-5 stars)

This teaches:

• How AI interprets instructions
• Which words work best
• How to refine your prompts

Advanced Techniques

Prompt Engineering Tricks:

Be specific about quantities:

• "Three cats playing" instead of "cats playing"

Specify perspective:

• "Bird's eye view of a city park"
• "Close-up of a butterfly on a flower"

Control mood with adjectives:

• "Cozy cabin in snowy woods" (warm feeling)
• "Abandoned cabin in dark forest" (spooky feeling)

Mention lighting:

• "Sunset lighting", "bright sunny day", "moonlight"

Reference art styles:

• "In the style of Studio Ghibli"
• "Like a comic book"
• "Impressionist painting"

Creative Challenges

Challenge 1: Recreate a Famous Painting Try to get AI to create something similar to the Mona Lisa, Starry Night, or other famous art

Challenge 2: Illustrate a Story

• Write a short story (3-5 sentences)
• Create 3 AI images to illustrate it
• Put them together as a picture book page

Challenge 3: Before and After

• "Messy bedroom" vs. "Clean organized bedroom"
• "Tiny seed" vs. "Giant tree"
• "Egg" vs. "Hatched baby dragon"

Challenge 4: Seasonal Transformations Create the same scene in different seasons:

• "Park with playground in spring"
• "Park with playground in summer"
• "Park with playground in autumn"
• "Park with playground in winter"

Important Ethics Discussions

Parent Conversation Points:

1. Ownership: "Who owns AI-created art?"

   • The person who wrote the prompt?
   • The company that made the AI?
   • The artists whose work trained the AI?

2. Artist Impact: "How does AI affect human artists?"

   • Is using AI art generation fair to artists?
   • When should you hire a human artist instead?
   • How can AI and human artists work together?

3. Disclosure: "Should you tell people when art is AI-generated?"

   • Yes! Always be transparent
   • Especially in school projects or contests
   • Why honesty matters

4. Training Data: "Where did the AI learn to create art?"

   • Trained on millions of images from the internet
   • Includes many artists' work
   • Raises questions about consent and credit

Extending the Project

Create a Gallery Exhibition:

• Print your favorite AI creations
• Write descriptions of each
• Host a family art show
• Discuss: "Is this real art?"

Compare AI vs. Human Art:

• Choose a subject
• Create one AI version
• Draw/paint your own version
• Compare: What's similar? Different? Which do you prefer?

Build an AI Art Portfolio:

• Select your best 10-20 AI creations
• Organize by theme or style
• Write artist statements
• Share with friends and family

What's the AI Actually Doing?

Simple Explanation: "The AI learned by looking at millions of images with descriptions. It learned patterns—like 'dogs usually have four legs' and 'cats have pointy ears' and 'sunsets are orange and pink.' When you give it a prompt, it combines those patterns to create something new."

Why It Sometimes Fails:

• Strange hands (AI struggles with hand anatomy)
• Weird text in images (AI can't really "write")
• Impossible objects (mixing concepts it doesn't understand)
• Missing details you requested (prompt wasn't clear enough)

---

Project 8: Build a Smart Music DJ with AI

Difficulty: 🟡 Intermediate | Time: ⏱️⏱️ Medium | Materials: 💻 🎤

What You'll Build

Create an AI that responds to sounds and creates music, or build a gesture-controlled DJ that plays different beats based on your movements.

What You'll Learn

• Audio recognition
• Sound classification
• Music creation basics
• Real-time AI response systems

Required Tools

• Teachable Machine (teachablemachine.withgoogle.com) - for sound classification
• Scratch (scratch.mit.edu) - for music creation
• Machine Learning for Kids - to connect them
• OR Google Magenta Studio for advanced music AI

Step-by-Step Instructions

Part 1: Train Your Sound Classifier

Step 1: Set Up Teachable Machine for Audio

• Go to teachablemachine.withgoogle.com
• Choose "Audio Project"
• You'll see a microphone input

Step 2: Create Sound Classes

Option A: Vocal Sounds DJ

• Class 1: "Bass" - Make low "boom" sound
• Class 2: "Snare" - Make sharp "tss" sound
• Class 3: "Melody" - Hum or sing a tune
• Class 4: "Background" - Silence/room noise

Option B: Found Sounds DJ

• Class 1: Clapping
• Class 2: Tapping table
• Class 3: Shaking something
• Class 4: Silence

Option C: Instrument DJ

• If you have instruments, record different ones:
• Guitar strum, piano key, drum hit, etc.

Step 3: Record Training Examples

• For each class, record 20-30 short samples (2 seconds each)
• Vary your sounds slightly
• Make them distinctive from each other
• Include background noise class

Step 4: Train Your Model

• Click "Train Model"
• Test it by making sounds and watching predictions
• Retrain if accuracy is low

Part 2: Connect to Music Creation

Step 5: Export Your Sound Model

• Click "Export Model"
• Get shareable link

Step 6: Set Up in Scratch

• Go to Machine Learning for Kids
• Create new project for sound recognition
• Import your Teachable Machine model

Step 7: Create Your DJ Interface

// Initialize
when green flag clicked
set [current_beat v] to [none]
forever
  set [detected_sound v] to (recognize sound from microphone)

  // Bass sound detected
  if <(detected_sound) = [Bass]> then
    play drum (1 v) for (0.25) beats
    change [bass_count v] by (1)
  end

  // Snare sound detected
  if <(detected_sound) = [Snare]> then
    play drum (2 v) for (0.25) beats
    change [snare_count v] by (1)
  end

  // Melody detected
  if <(detected_sound) = [Melody]> then
    play note (60 v) for (0.5) beats
  end
end

Step 8: Add Visual Feedback

// Make visuals respond to sounds
if <(detected_sound) = [Bass]> then
  change color effect by (25)
  change size by (20)
  wait (0.1) seconds
  change size by (-20)
end

if <(detected_sound) = [Snare]> then
  set [ghost v] effect to (50)
  wait (0.1) seconds
  set [ghost v] effect to (0)
end

Step 9: Create Beat Patterns

// When space key pressed, start automatic beat
when [space v] key pressed
repeat (8)
  play drum (1 v) for (0.5) beats  // Bass
  play drum (2 v) for (0.5) beats  // Snare
  play drum (1 v) for (0.5) beats  // Bass
  play drum (2 v) for (0.5) beats  // Snare
end

Part 3: Make It Interactive

Add Features:

1. Loop Recording

// Record a sound loop
when [r v] key pressed
say [Recording!] for (1) seconds
// Record 4 beats worth of your sounds
// Play them back on loop

when [p v] key pressed
forever
  // Playback your recorded sounds
end

2. Different Beats

when [1 v] key pressed
set [beat_style v] to [hip-hop]

when [2 v] key pressed
set [beat_style v] to [rock]

when [3 v] key pressed
set [beat_style v] to [electronic]

// Change drum sounds based on beat_style

3. Tempo Control

when [up arrow v] key pressed
change [tempo v] by (10)

when [down arrow v] key pressed
change [tempo v] by (-10)

set tempo to (tempo) bpm

Creative Variations

Project Variation 1: Emotion-Based Music

• Train AI to recognize emotions from voice
• Happy voice → upbeat music
• Sad voice → slow, minor key music
• Excited voice → fast, energetic music
• Calm voice → ambient, peaceful music

Project Variation 2: Gesture DJ

• Use Teachable Machine for poses
• Different hand positions trigger different instruments
• Arms up = louder
• Arms down = quieter
• Different poses = different beats

Project Variation 3: Story Soundscape

• Create sound effects for a story
• Snap fingers = thunder
• Blow = wind sound
• Clap = footsteps
• Whistle = bird song

Using Google Magenta (Advanced)

For older kids or those ready for a challenge:

What is Magenta?

• Google's AI music and art project
• Uses machine learning to create music
• More advanced than Scratch

Cool Magenta Projects:

• Continue a Melody: Start a tune, AI continues it
• Drumify: Turn any sound into drums
• Piano Genie: Play piano with just 8 keys, AI makes it sound good
• NSynth: Create completely new instrument sounds

Access Magenta:

• Visit magenta.withgoogle.com
• Try the demos online
• No coding required for basic tools

Performance Ideas

Host a DJ Show:

1. Create a setlist of sounds
2. Practice transitions between beats
3. Invite family to watch
4. Record your performance
5. Explain how the AI works

Collaboration Challenge:

• One person makes bass sounds
• Another makes snare sounds
• Third person adds melody
• AI combines into music!

What You're Actually Learning

Real DJ/Music Production Skills:

• Beat matching
• Rhythm and timing
• Sound mixing
• Performance techniques
• Audio engineering basics

AI Concepts:

• Audio classification
• Real-time inference
• Sensor input (microphone)
• Pattern recognition in sound

Troubleshooting

"AI confuses my sounds"

• Make sounds more distinctive
• Add more training examples
• Reduce background noise
• Record in quieter environment

"Sounds trigger at wrong times"

• Adjust confidence threshold
• Add "silence" class with lots of examples
• Retrain with more varied examples

"Music sounds choppy"

• Reduce prediction frequency
• Smooth transitions in code
• Add delay buffers
• Simplify simultaneous sounds

Extension Challenges

Easy:

• Add 5+ more sound categories
• Create intro and outro sequences
• Add visual themes for each beat style
• Make score counter based on detected sounds

Medium:

• Record and loop user's beats
• Create AI that suggests next sound
• Build multiple preset beat patterns
• Add tempo detection and auto-sync

Advanced:

• Integrate actual music theory (scales, chords)
• Build AI that composes original melodies
• Create multiplayer mode (2+ DJs)
• Connect MIDI instruments for real music creation

---

Project 9: Train an AI Robot (Physical Project)

Difficulty: 🔴 Advanced | Time: ⏱️⏱️⏱️ Long | Materials: 💻 🧱 (💰 optional)

What You'll Build

Program a physical robot using AI principles, teaching it to navigate, recognize objects, or respond to its environment. This combines coding, engineering, and machine learning!

What You'll Learn

• Robotics basics
• Sensor integration
• Path planning and navigation
• Physical computing
• Hardware + AI combination

Required Materials

Budget-Friendly Options:

• Micro:bit + AI accessories ($15-50)
  • Micro:bit board
  • Robot car kit
  • DFRobot SEN0539 AI camera

Mid-Range Options:

• LEGO MINDSTORMS Robot Inventor (~$350)
  • All-in-one kit
  • Great building instructions
  • Visual programming

Advanced Options:

• Raspberry Pi + Camera Module (~$100-150)
  • More flexibility
  • Real AI/ML capabilities
  • Requires more setup

For This Guide: We'll use LEGO MINDSTORMS (most kid-friendly)

Step-by-Step Instructions

Phase 1: Build Your Robot Base

Step 1: Choose Your Robot Design

LEGO MINDSTORMS includes designs for:

• BLAST: Shooting robot (for target recognition)
• CHARLIE: Walking humanoid (for gesture recognition)
• TRICKY: Sports robot (for ball tracking)
• M.V.P.: Multi-purpose vehicle (for navigation)
• GELO: Four-legged creature (for terrain adaptation)

Recommendation: Start with M.V.P. (driving robot) for navigation projects

Step 2: Build According to Instructions

• Follow included building guide
• Takes 1-2 hours
• Test motors and sensors work
• Learn robot's capabilities

Phase 2: Basic Programming

Step 3: Connect to Programming App

• Download LEGO MINDSTORMS Robot Inventor App
• Connect robot via Bluetooth
• Explore block-based programming interface

Step 4: Test Basic Movements

// Make robot drive forward
Start Program
  Motor A: Power 75, Degrees 360
  Motor B: Power 75, Degrees 360
End Program

// Make robot turn
Start Program
  Motor A: Power 75, Degrees 360
  Motor B: Power -75, Degrees 360
End Program

Step 5: Test Sensors

• Distance Sensor: Detects obstacles
• Color Sensor: Recognizes colors and light
• Force Sensor: Detects pressure/touch

// Obstacle detection
Repeat Forever
  If Distance < 10cm Then
    Motor A: Stop
    Motor B: Stop
    Wait 1 second
  Else
    Motor A: Power 50
    Motor B: Power 50
  End
End Repeat

Phase 3: Add AI-Style Behaviors

Project A: Line Following (Machine Learning Basics)

What It Teaches: How robots can learn paths

Step 6: Set Up Track

• Use black tape on light floor (or vice versa)
• Create a path with turns and curves

Step 7: Program Line Following

Repeat Forever
  Read Color Sensor

  If Color = Black Then
    // On line, go straight
    Motor A: Power 50
    Motor B: Power 50

  Else If Color = White Then
    // Off line to right, turn left
    Motor A: Power 20
    Motor B: Power 50
  End
End Repeat

Step 8: Improve with Calibration

• Have robot record "black" and "white" values
• Adjust turns based on how far off-line it is
• Add speed control for sharp vs. gentle curves

This is like training AI!

• Robot samples environment (gathers data)
• Adjusts behavior based on input (learns)
• Improves with practice (optimization)

Project B: Object Recognition and Sorting

What It Teaches: Classification (like image recognition AI)

Step 9: Set Up Objects to Recognize

• Use LEGO bricks in 3 colors (red, blue, green)
• Place them in robot's path

Step 10: Program Recognition

Initialize
  Set Red_Count = 0
  Set Blue_Count = 0
  Set Green_Count = 0
End Initialize

Repeat Until Button Pressed
  // Scan for object
  Read Color Sensor

  If Color = Red Then
    Say "Red brick found!"
    Red_Count = Red_Count + 1
    // Push red brick to right
    Motor C: Power 100, Degrees 180

  Else If Color = Blue Then
    Say "Blue brick found!"
    Blue_Count = Blue_Count + 1
    // Push blue brick to left
    Motor C: Power -100, Degrees 180

  Else If Color = Green Then
    Say "Green brick found!"
    Green_Count = Green_Count + 1
    // Push green brick forward
    Motor A: Power 50, Degrees 180
    Motor B: Power 50, Degrees 180
  End

  // Move to next object
  Motor A: Power 30, Degrees 360
  Motor B: Power 30, Degrees 360
End Repeat

// Report results
Say "Found:"
Say Red_Count & " red"
Say Blue_Count & " blue"
Say Green_Count & " green"

Step 11: Add Learning

• Track which colors appear most
• Adjust search pattern based on previous finds
• "Predict" where next object might be

Project C: Maze Solver (Pathfinding AI)

What It Teaches: How AI navigates unknown environments

Step 12: Create Simple Maze

• Use cardboard or books as walls
• Include dead ends and multiple paths
• Mark start and end points

Step 13: Program Basic Maze Navigation

// Right-hand rule (always follow right wall)
Repeat Until [at end]
  Read Distance Sensor

  If Right Side Open Then
    Turn Right 90°
    Move Forward 10cm

  Else If Front Open Then
    Move Forward 10cm

  Else
    Turn Left 90°
  End
End Repeat

Say "Maze solved!"

Step 14: Add Memory (Path Learning)

Initialize
  Create Empty List "Path"
End Initialize

While Exploring
  Record Each Move to "Path"
  // Examples: "Forward", "Right", "Left", "Back"
End While

When Maze Solved
  // Now robot knows the solution!
  Say "I learned the maze!"
End

When [run again] pressed
  // Follow learned path
  For Each Move in "Path"
    Execute Move
  End For

  Say "I remember the way!"
End

This is AI in action!

• Exploration (data gathering)
• Recording (training data)
• Recall (using learned model)
• Optimization (finding shortest path)

Phase 4: Advanced AI Integration

For Raspberry Pi Users:

Step 15: Add Camera Vision

• Attach Raspberry Pi Camera Module
• Install TensorFlow Lite
• Use pre-trained models for object detection

Example Uses:

• Recognize family members
• Identify toys vs. obstacles
• Read simple signs or symbols
• Follow colored objects

Step 16: Connect to AI Services

• Link to Google Cloud Vision
• Use speech recognition APIs
• Control with voice commands
• Get weather/info from internet

Real Robots in Action

Tell Your Kids:

"Robots like yours (but more complex) are used to:

• Warehouses: Amazon robots sort packages (like your color sorter!)
• Hospitals: Robots deliver medicine using pathfinding (like your maze solver!)
• Mars: NASA rovers navigate unknown terrain (like your explorer!)
• Oceans: Underwater robots inspect pipelines (using computer vision!)
• Farms: Agricultural robots identify ripe crops (like object recognition!)"

Troubleshooting

Robot doesn't move smoothly:

• Check battery level
• Ensure motors are firmly connected
• Calibrate motor speeds (may not be identical)
• Reduce power for smoother movements

Sensors give inconsistent readings:

• Test in consistent lighting
• Clean sensor lenses
• Adjust sensor positioning
• Add delays between readings

Programs don't work as expected:

• Test each section separately
• Add status lights/sounds for debugging
• Start simple, add complexity gradually
• Check wireless connection strength

Extension Projects

Easy:

• Dance robot (responds to music)
• Pet robot (reacts to touch/voice)
• Guard robot (alerts when detecting motion)
• Delivery robot (brings items from point A to B)

Medium:

• Soccer robot (finds and kicks ball toward goal)
• Artist robot (draws patterns or pictures)
• Rescue robot (finds objects in maze-like environment)
• Battle bot (competes in challenges)

Advanced:

• Self-balancing robot (like a Segway)
• Robotic arm with object manipulation
• Swarm robotics (multiple robots coordinating)
• Competition robot (RoboCup, FIRST LEGO League)

Competitions and Communities

Join the Robot Community:

• FIRST LEGO League: Global competition for ages 9-16
• RoboCup Junior: International robot soccer
• Local robotics clubs: Check libraries, maker spaces
• Online communities: Reddit r/LEGO, robotics forums

Parent Tips

Budget Considerations:

• Start simple (Micro:bit) before investing in expensive kits
• Borrow from libraries or schools if possible
• Used LEGO MINDSTORMS sets work great
• Share costs with friends/neighbors

Learning Together:

• You don't need robotics expertise
• Learn alongside your child
• Celebrate failures as learning opportunities
• Focus on problem-solving process, not perfection

Safety Notes:

• Supervise use of small parts (choking hazard for younger siblings)
• Be careful with motors (pinch points)
• Protect electronics from water
• Teach proper handling of batteries

---

Project 10: Create an AI Study Buddy

Difficulty: 🟡 Intermediate | Time: ⏱️⏱️⏱️ Long | Materials: 💻

What You'll Build

Build an interactive AI-powered study helper that quizzes you on topics, tracks progress, and adapts difficulty based on performance—like having a personal tutor!

What You'll Learn

• Adaptive learning systems
• Question banks and databases
• Progress tracking
• Difficulty adjustment algorithms
• Educational game design

Why This Project Rocks

You're building a tool you'll actually use! Plus, it teaches how educational AI (like Khan Academy, Duolingo) works behind the scenes.

Step-by-Step Instructions

Phase 1: Choose Your Subject

Pick a topic you're learning in school:

• Math: Multiplication, fractions, algebra
• Science: Parts of a cell, planets, chemical elements
• Geography: Countries, capitals, landmarks
• History: Dates, events, important figures
• Language: Vocabulary, spelling, grammar
• Other: Anything you need to study!

Example: We'll build a "Multiplication Master" study buddy

Phase 2: Design Your Study System

Step 1: Plan Your Features

Core Features:

• Question bank with multiple difficulty levels
• Quiz mode with instant feedback
• Score tracking
• Progress dashboard
• Encouragement messages
• Adaptive difficulty (gets harder as you improve)

Optional Features:

• Timed challenges
• Multiplayer mode (compete with friends/family)
• Achievements/badges
• Study streaks (days in a row)
• Difficulty selection (easy/medium/hard)

Step 2: Create Your Question Bank

For Multiplication (example):

Level 1 - Easy (0-5 tables):

• 2 × 3 = ?
• 1 × 5 = ?
• 4 × 2 = ? (50 questions)

Level 2 - Medium (0-10 tables):

• 7 × 6 = ?
• 8 × 9 = ?
• 5 × 7 = ? (100 questions)

Level 3 - Hard (0-12 tables + mixed):

• 11 × 9 = ?
• 12 × 8 = ?
• Mixed: 7 × 11 = ? (100 questions)

Phase 3: Build in Scratch

Step 3: Set Up Variables

// User Progress
(username)
(current_level) // 1, 2, or 3
(streak_days)
(total_score)
(questions_answered)
(correct_answers)
(accuracy_percent)

// Current Question
(question)
(correct_answer)
(user_answer)

// Settings
(difficulty)
(questions_per_session)
(time_limit)

Step 4: Create Lists for Questions

[Easy_Questions v] list
[Easy_Answers v] list
[Medium_Questions v] list
[Medium_Answers v] list
[Hard_Questions v] list
[Hard_Answers v] list

Step 5: Program the Welcome Screen

when green flag clicked
// Welcome animation
say [Welcome to Study Buddy! 📚] for (2) seconds
ask [What's your name?] and wait
set [username v] to (answer)

// Load or create profile
if <[username v] in saved profiles> then
  // Load existing progress
  say (join [Welcome back, ] (username)) for (2) seconds
  say (join [Current level: ] (current_level)) for (2) seconds
  say (join [Accuracy: ] (accuracy_percent) [%]) for (2) seconds
else
  // New user
  say (join [Nice to meet you, ] (username)) for (2) seconds
  set [current_level v] to (1)
  set [total_score v] to (0)
  say [Let's start with level 1!] for (2) seconds
end

// Main menu
ask [What would you like to do? (1) Practice (2) View Progress (3) Settings] and wait

Step 6: Build Quiz Logic

// Practice Mode
when [1] selected
say [Let's practice!] for (1) seconds
set [questions_answered v] to (0)
set [correct_answers v] to (0)

repeat (10) // 10 questions per session
  // Generate question based on level
  if <(current_level) = [1]> then
    set [question v] to (item (random 1 to length of [Easy_Questions v]) of [Easy_Questions v])
    // Get corresponding answer
  end

  // Ask question
  ask (join (question) [ = ?]) and wait
  set [user_answer v] to (answer)

  // Check answer
  if <(user_answer) = (correct_answer)> then
    say [✓ Correct! Great job!] for (1) seconds
    change [correct_answers v] by (1)
    change [total_score v] by (10)
    play sound [ding v]
  else
    say (join [✗ Not quite. The answer is ] (correct_answer)) for (2) seconds
    say [Let's try another one!] for (1) seconds
    play sound [whoops v]
  end

  change [questions_answered v] by (1)

  // Show progress
  say (join [Question ] (questions_answered) [ of 10]) for (0.5) seconds
end

// Calculate accuracy
set [accuracy_percent v] to ((correct_answers) / (questions_answered) * 100)

// Show results
say (join [Session complete! Score: ] (correct_answers) [ out of 10]) for (3) seconds
say (join [Accuracy: ] (accuracy_percent) [%]) for (2) seconds

// Adjust difficulty (AI learning!)
if <(accuracy_percent) > [80]> and <(current_level) < [3]> then
  say [Wow! You're ready for the next level!] for (2) seconds
  change [current_level v] by (1)
else if <(accuracy_percent) < [40]> and <(current_level) > [1]> then
  say [Let's practice this level a bit more] for (2) seconds
end

Step 7: Add Progress Tracking

// View Progress Option
when [2] selected
// Show dashboard
say (join [Total Questions Answered: ] (questions_answered)) for (2) seconds
say (join [Overall Accuracy: ] (accuracy_percent) [%]) for (2) seconds
say (join [Current Level: ] (current_level)) for (2) seconds
say (join [Total Score: ] (total_score) [ points]) for (2) seconds
say (join [Study Streak: ] (streak_days) [ days]) for (2) seconds

// Visual progress bar
clear
set pen size to (10)
set pen color to [green]
pen down
// Draw progress bar based on accuracy
repeat (accuracy_percent) times
  move (2) steps
end
pen up

// Goals
if <(questions_answered) < [100]> then
  say [Goal: Answer 100 questions total!] for (2) seconds
else if <(accuracy_percent) < [90]> then
  say [Goal: Reach 90% accuracy!] for (2) seconds
else
  say [You're a study superstar! 🌟] for (2) seconds
end

Step 8: Implement Adaptive Difficulty

// Smart difficulty adjustment
define adjust_difficulty
  // Too easy? Level up!
  if <(accuracy_percent) > [85]> and <(consecutive_correct) > [5]> then
    if <(current_level) < [3]> then
      change [current_level v] by (1)
      say [Level up! 📈 Questions will be harder now] for (2) seconds
      set [consecutive_correct v] to (0)
    else
      say [You've mastered this topic! 🏆] for (2) seconds
      // Unlock challenge mode or new topic
    end
  end

  // Too hard? Provide support
  if <(accuracy_percent) < [50]> and <(consecutive_wrong) > [3]> then
    say [Let's review! I'll show you how to solve these] for (2) seconds
    // Show explanation/tutorial
    set [consecutive_wrong v] to (0)
  end

  // Just right? Keep going!
  if <(accuracy_percent) >= [50]> and <(accuracy_percent) <= [85]> then
    say [You're making great progress! Keep it up!] for (2) seconds
  end
end

Phase 4: Add Engagement Features

Step 9: Create Achievement System

// Check for achievements after each session
define check_achievements

// Perfect Score Achievement
if <(correct_answers) = (questions_answered)> then
  if not <[perfect_score_earned] = [true]> then
    say [🏆 Achievement Unlocked: Perfect Score!] for (3) seconds
    set [perfect_score_earned v] to [true]
    change [total_score v] by (50) // bonus points
  end
end

// Speed Demon Achievement
if <(average_time_per_question) < [5]> then
  if not <[speed_demon_earned] = [true]> then
    say [⚡ Achievement Unlocked: Speed Demon!] for (3) seconds
    set [speed_demon_earned v] to [true]
  end
end

// Persistent Learner
if <(streak_days) = [7]> then
  say [🔥 7-Day Streak! You're on fire!] for (3) seconds
else if <(streak_days) = [30]> then
  say [🌟 30-Day Streak! Incredible dedication!] for (3) seconds
end

// Level Master
if <(current_level) = [3]> and <(accuracy_percent) > [90]> then
  say [👑 Achievement Unlocked: Master of Multiplication!] for (3) seconds
end
end

Step 10: Add Encouraging AI Messages

// Smart encouragement based on performance
define encourage_student

// Struggling
if <(consecutive_wrong) > [2]> then
  pick random encouragement from list:
  - "Don't give up! Everyone learns at their own pace"
  - "Mistakes help us learn! Let's try again"
  - "You're closer than you think!"
end

// Improving
if <(last_accuracy) < (current_accuracy)> then
  - "Nice improvement! Your hard work is paying off!"
  - "You're getting better! Keep going!"
  - "Progress! That's what learning is all about!"
end

// Doing well
if <(accuracy_percent) > [75]> then
  - "Outstanding work!"
  - "You're really getting the hang of this!"
  - "Keep up the excellent work!"
end

// Mixed results
if <(accuracy_percent) >= [50]> and <(accuracy_percent) <= [75]> then
  - "Good effort! Practice makes perfect"
  - "You're on the right track!"
  - "Nice work! Want to try a few more?"
end
end

Phase 5: Advanced Features

Adding Explanation Mode

When student gets wrong answer:

if <(user_answer) ≠ (correct_answer)> then
  ask [Want to see how to solve this? (yes/no)] and wait

  if <(answer) = [yes]> then
    // Break down the problem
    say (join [Let's think about ] (question)) for (2) seconds

    // For multiplication example
    say [We can use repeated addition!] for (2) seconds
    say (join (first_number) [ groups of ] (second_number)) for (2) seconds

    // Visual demonstration
    // Draw groups or use animation

    say (join [So the answer is ] (correct_answer)) for (2) seconds
    ask [Want to try a similar problem?] and wait
  end
end

Adding Timed Challenge Mode

// Speed round
when [timed challenge] selected
say [Quick! Answer as many as you can in 60 seconds!] for (2) seconds
set [time_remaining v] to (60)
set [quick_score v] to (0)

// Start timer
broadcast [start timer v]

repeat until <(time_remaining) = [0]>
  // Generate quick questions
  // Simplified interface for speed
  // Track correct answers
end

say (join [Time's up! You answered ] (quick_score) [ questions correctly!]) for (3) seconds

Adding Multiplayer Mode

// Two player competition
when [multiplayer] selected
ask [Player 1 name?] and wait
set [player1_name v] to (answer)
ask [Player 2 name?] and wait
set [player2_name v] to (answer)

say [You'll take turns answering questions. First to 10 wins!] for (3) seconds

set [player1_score v] to (0)
set [player2_score v] to (0)
set [current_player v] to (1)

repeat until <(player1_score) = [10]> or <(player2_score) = [10]>
  if <(current_player) = [1]> then
    say (join (player1_name) ['s turn!]) for (1) seconds
    // Ask question
    // Update player1_score if correct
    set [current_player v] to (2)
  else
    say (join (player2_name) ['s turn!]) for (1) seconds
    // Ask question
    // Update player2_score if correct
    set [current_player v] to (1)
  end
end

// Declare winner
if <(player1_score) = [10]> then
  say (join (player1_name) [ wins! 🎉]) for (3) seconds
else
  say (join (player2_name) [ wins! 🎉]) for (3) seconds
end

Testing Your Study Buddy

Test Cases:

1. New User Experience

   • Clear welcome message?
   • Easy to understand instructions?
   • Appropriate starting difficulty?

2. Learning Progression

   • Does difficulty increase when doing well?
   • Does it provide support when struggling?
   • Are level transitions smooth?

3. Motivation

   • Are encouragement messages helpful?
   • Do achievements feel rewarding?
   • Is progress visible and satisfying?

4. Accuracy

   • Are questions and answers correct?
   • Does scoring calculate properly?
   • Are stats tracked accurately?

Real-World Connections

Tell Your Kids:

"Your Study Buddy uses the same concepts as:

• Duolingo: Adapts language lessons to your level
• Khan Academy: Tracks your math progress and adjusts difficulty
• Quizlet: Creates personalized study sessions
• IXL: Intelligent practice that responds to your performance
• Prodigy: Math game that adjusts based on answers"

Extensions

Easy Extensions:

• Add more subjects (create separate question banks)
• Include images or diagrams with questions
• Add sound effects for correct/incorrect
• Create different visual themes

Medium Extensions:

• Export progress reports (daily/weekly)
• Add parent dashboard to see child's progress
• Create custom question sets (student or parent can add)
• Integrate spaced repetition (reviews harder questions more often)

Advanced Extensions:

• Use actual machine learning to predict which questions to ask
• Natural language processing (allow typed-out answers for word problems)
• Connect to online APIs for infinite question generation
• Build web version with user accounts and cloud save

Sharing Your Study Buddy

Help Others Learn:

• Share your project on Scratch
• Create tutorial video showing how it works
• Let classmates use it for studying
• Customize for different subjects/grades
• Submit to school coding competition

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Making the Most of These Projects

Tips for Parents

1. Let Them Lead

• Ask: "Which project interests you most?"
• Follow their curiosity
• Don't insist on completing projects they're not enjoying

2. Embrace Failure

• When AI doesn't work: "Why do you think that happened?"
• Bugs are learning opportunities
• Celebrate problem-solving, not just success

3. Connect to Real World

• Discuss: "Where have you seen this type of AI?"
• Visit businesses/places using similar technology
• Watch age-appropriate documentaries together

4. Set Realistic Expectations

• 🟢 Beginner projects: 1-2 sessions
• 🟡 Intermediate: 3-5 sessions
• 🔴 Advanced: 1-2 weeks with breaks

Take breaks! Frustration means it's time to step away.

5. Document the Journey

• Take photos/videos
• Write about what they learned
• Create a project portfolio
• Share with relatives (kids love showing off!)

Tips for Kids

1. Start Small Don't try the most complex project first! Build confidence with easier ones.

2. Read Instructions Twice

• First read: get the big picture
• Second read: follow step-by-step

3. Test as You Go Don't write all the code then test. Test each part!

4. Ask for Help Stuck for more than 15 minutes? Ask a parent, teacher, or search online.

5. Make It Yours Once basic project works, customize it! Add your own ideas, themes, features.

6. Teach Someone Else Best way to learn? Explain your project to a friend or family member!

Building a Portfolio

Why Create a Portfolio?

• Shows your growth over time
• Useful for school applications
• Great for showcasing skills
• Fun to look back on later

What to Include:

For Each Project:

• Project name and date
• Photos or screenshots
• What you built: Brief description
• What you learned: Key concepts
• Challenges faced: Problems you solved
• What you'd do differently: Reflection

Portfolio Formats:

• Physical binder with printed pages
• Digital slideshow (Google Slides, PowerPoint)
• Website (with parent help)
• Video demonstrations
• GitHub repository (for code projects)

Next Steps After These Projects

Ready for More?

Online Courses:

• Code.org: Free CS courses including AI
• Scratch MIT: Official tutorials and community
• Khan Academy: Free CS and math
• CodaKid: Paid courses in game dev and AI

Books:

• "Hello Ruby" series by Linda Liukas
• "AI for Kids" by Dheeraj Mehrotra
• "Computational Fairy Tales" by Jeremy Kubica

Competitions:

• First LEGO League: Robotics competitions
• Coolest Projects: Showcase your tech creations
• Technovation: Girls coding competition (apps/AI)
• Congressional App Challenge: Create apps for your community

Local Opportunities:

• Library coding clubs
• School robotics teams
• Maker spaces
• CoderDojo chapters
• 4-H STEM programs

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Conclusion: From Consumer to Creator

These 10 projects transform your child from someone who uses AI to someone who builds with AI. That shift in mindset—from passive consumer to active creator—is one of the most valuable gifts you can give them.

Every project here teaches multiple concepts:

• Technical skills: Coding, training models, problem-solving
• Computational thinking: Breaking problems into steps, pattern recognition
• Creativity: Designing solutions, customizing projects
• Persistence: Debugging, iterating, improving
• Ethics: Understanding bias, privacy, and responsible AI use

But most importantly, these projects teach that AI isn't magic—it's a tool humans create and control. When your child trains an AI model, they understand its limitations. When they see their model fail, they learn about bias and training data. When they make it work, they feel empowered.

That empowerment—that sense of "I can build this!"—will serve them far beyond these specific projects. It's the foundation for a lifetime of learning, creating, and innovating in an increasingly AI-powered world.

So pick a project, clear some time, and start building. The future is waiting for what your child will create.

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Quick Reference Guide

Project Difficulty & Time Overview

Essential Tools Checklist

Free Tools:

• Computer with internet
• Webcam (built-in or external)
• Microphone (built-in or external)
• Scratch account (scratch.mit.edu)
• Teachable Machine (no account needed)
• Code.org access

Optional Tools:

• Machine Learning for Kids account
• Robotics kit (LEGO, Micro:bit, etc.)
• Printer (for documentation/portfolio)
• Drawing supplies (for AI art projects)

Troubleshooting Common Issues

Problem: AI model isn't accurate Solution: Add more training examples (50+ per category), ensure variety

Problem: Webcam/mic not working Solution: Check browser permissions, try different browser

Problem: Project too hard Solution: Start with easier project, build confidence first

Problem: Lost interest mid-project Solution: Take break, try different project, or simplify current one

Problem: Want to show off but project incomplete Solution: Demo what works so far! Explain what you're still building

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