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Grades 4-6 Computer Science 60 Minutes

Lesson 2: Teaching Machines - The AI Training Game

Students become AI trainers in this kinesthetic activity, learning about machine learning through a fun, interactive game that demonstrates how AI systems improve with data.

Learning Objectives

  • Explain the concept of machine learning and how AI systems improve through training with examples
  • Demonstrate understanding of pattern recognition by identifying common features in training data
  • Experience firsthand how more training data leads to better AI performance and fewer errors
  • Collaborate effectively in groups to train a human AI, demonstrating teamwork and communication skills
  • Connect the activity experience to real-world AI applications like image recognition and voice assistants

Standards Alignment

  • CSTA 2-AP-10: Use flowcharts and/or pseudocode to address complex problems as algorithms
  • CSTA 2-AP-13: Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs
  • CSTA 2-IC-20: Compare tradeoffs associated with computing technologies that affect people's everyday activities and career options
  • NGSS MS-ETS1-2: Evaluate competing design solutions using a systematic process to determine how well they meet the criteria and constraints
  • CCSS.MATH.PRACTICE.MP2: Reason abstractly and quantitatively
  • CCSS.MATH.PRACTICE.MP3: Construct viable arguments and critique the reasoning of others

Materials Needed

  • Training card sets (4 categories: Animals, Vehicles, Food, Sports) - 20 cards per category, printable PDF included in downloadable materials
  • Blindfolds or eye masks (one per AI student) - alternatively, students can close their eyes or face away from cards
  • Timer or stopwatch for each group (can use classroom clock or smartphones)
  • Score sheets and pencils for recording accuracy (included in downloadable materials)
  • Chart paper or whiteboard for creating Training Rules and displaying group results
  • Student reflection worksheets (included in downloadable materials)
  • Graph templates for data visualization (included in downloadable materials)
  • Optional: Video camera or tablet to record game play for class review and discussion
  • Optional: Computer or tablet to demonstrate real AI tools like Quick, Draw! by Google or Teachable Machine

Lesson Procedure

  1. Introduction and Concept Building (10 minutes)

    Warm-Up Discussion: Begin by asking students: "Have you ever taught someone how to do something? What did you do to help them learn?" Allow 2-3 students to share examples. This activates prior knowledge about teaching and learning.

    Connect to previous learning (if applicable) or introduce the concept: "We've learned that AI can solve problems, but today we'll discover HOW AI actually learns. You're going to experience machine learning firsthand by becoming both AI trainers and AI systems!"

    Key Concepts to Introduce:

    • Training Data: The examples we give AI to learn from, similar to flashcards you use when studying for a test. The more examples, the better the AI learns.
    • Pattern Recognition: Finding what's similar across multiple examples. For instance, all cats have whiskers, pointy ears, and say "meow" - these are patterns that help us identify cats.
    • Accuracy: How often the AI gets the right answer, just like getting 9 out of 10 questions correct on a quiz means 90% accuracy.
    • Machine Learning: When AI improves its performance by learning from examples rather than being explicitly programmed with every rule.

    Visual Demonstration: On the board, draw a simple progression showing: "1 example → some understanding", "5 examples → better understanding", "20 examples → strong understanding". Explain that today's activity will prove this concept.

    Explain the Activity: "Some of you will become 'AI systems' that need training to recognize and categorize objects. Others will be 'AI trainers' who teach the AI by describing examples. We'll measure how the AI improves with more training!"

  2. Game Setup and Rules Explanation (5 minutes)

    Group Formation: Divide class into groups of 4-5 students. Assign roles (students will rotate roles if time permits):

    • The AI (1 student): Will be blindfolded and must learn to categorize objects based only on verbal descriptions
    • Lead Trainer (1 student): Primary describer, ensures consistent descriptions
    • Assistant Trainer (1-2 students): Helps describe items, identifies patterns to emphasize
    • Data Recorder (1 student): Tracks accuracy, times rounds, and records observations

    Game Rules:

    1. Round 1 - Small Training Set: Trainers show AI only 5 examples from each of the 4 categories (20 total examples), describing specific features without showing pictures
    2. Testing Phase 1: Present AI with 10 completely new items (not used in training). AI must categorize each one. Record how many correct.
    3. Round 2 - Expanded Training Set: Trainers show AI 10 additional examples from each category (40 more examples), building on what was learned
    4. Testing Phase 2: Present AI with 10 different new test items. Compare accuracy to Phase 1.

    Important Trainer Guidelines:

    • Be specific with features: "This animal has four legs, soft fur, whiskers, and says meow" (not just "it's a cat")
    • Point out patterns across examples: "Notice all the vehicles in this category have wheels and engines"
    • Use consistent terminology for similar features
    • Never show pictures to the AI - verbal descriptions only!
    • The AI can ask clarifying questions during training

    Success Metric: Groups should see at least a 20-30% improvement in accuracy from Round 1 to Round 2. This demonstrates the power of more training data!

  3. Game Play - Round 1 with Limited Training (15 minutes)

    Training Phase (7 minutes):

    • Blindfold the AI student (or have them face away from cards)
    • Trainers describe 5 items from each category (Animals, Vehicles, Food, Sports)
    • AI listens carefully and can ask clarifying questions: "Does it have wheels?" "What color is it?"
    • Encourage the AI to take mental notes or have Data Recorder write down key patterns
    • Teacher circulates among groups, listening for quality and specificity of descriptions
    • Provide feedback: "Try describing more specific features" or "That's a great observation about the pattern!"

    Testing Phase (8 minutes):

    • Trainers select 10 cards not used in training (2-3 from each category)
    • Hold up each card and ask: "What category is this?"
    • AI makes a guess based solely on the verbal description of the test item
    • After each guess, reveal if correct or incorrect (this is feedback for the AI)
    • Data recorder tracks each answer: mark correct with checkmark, incorrect with X
    • Calculate accuracy: (Number Correct ÷ 10) × 100 = Accuracy Percentage

    Quick Discussion (1 minute): Before moving to Round 2, ask each group: "Why was this challenging? What mistakes did your AI make? What patterns are you starting to notice?" This reflection helps students think like data scientists.

  4. Game Play - Round 2 with Enhanced Training (15 minutes)

    Additional Training Phase (7 minutes):

    • Trainers provide 10 MORE examples per category (40 additional training examples total)
    • AI student can now ask specific questions about categories they confused in Round 1: "What's the difference between basketball and soccer ball?"
    • Trainers should refine their descriptions based on Round 1 errors: "I notice you confused cars and trucks. Cars are smaller and usually seat 4-5 people..."
    • Emphasize pattern identification across ALL examples: "After seeing 15 animals, what do they all have in common?"
    • Encourage the AI to create mental "rules" for each category

    Second Testing Phase (8 minutes):

    • Present 10 completely new test items (different from both training sets and Round 1 test)
    • Use same testing procedure as Round 1
    • AI makes predictions; trainers provide immediate feedback
    • Data recorder carefully tracks all answers
    • Calculate Round 2 accuracy percentage
    • Celebrate improvements! If accuracy went up, the AI learned successfully!

    Data Analysis and Visualization:

    • Groups create a simple bar graph showing Round 1 accuracy vs. Round 2 accuracy
    • Calculate the improvement: Round 2 % - Round 1 % = Improvement
    • Calculate class average improvement across all groups
    • Identify which categories were easiest to learn (highest accuracy) and which were hardest (lowest accuracy)
    • Post group results on the board for whole-class comparison
  5. Class Discussion and Real-World AI Connections (10 minutes)

    Guided Discussion Questions:

    • "What happened to your accuracy between Round 1 and Round 2? Why do you think that happened?" (Expected answer: More examples = better learning)
    • "What patterns did the AI students identify to help them categorize items correctly?"
    • "What made it easier or harder to train your AI? What made you a better trainer?"
    • "How is this game similar to training a real AI system like Siri or Alexa?"
    • "What do you think would happen if we did Round 3 with 100 examples? Round 4 with 1,000 examples?" (Diminishing returns concept)
    • "Did anyone's AI get worse in Round 2? Why might that happen?" (Introduce concept of overfitting if relevant)

    Connect to Real AI Applications:

    • Image Recognition: "Facebook's photo tagging feature is trained on billions of photos to recognize faces. That's why it can identify you in photos automatically!"
    • Voice Assistants: "Alexa, Siri, and Google Assistant are trained on millions of voice samples from people with different accents, ages, and speech patterns."
    • Spam Filters: "Your email system learns from millions of examples of spam messages vs. legitimate messages to protect your inbox."
    • Recommendation Systems: "Netflix learns from billions of viewing choices to recommend shows you'll like. YouTube does the same with videos!"
    • Self-Driving Cars: "Cars that drive themselves are trained on millions of miles of driving data to recognize stop signs, pedestrians, and other vehicles."

    Key Takeaways to Write on Board:

    1. More training data → Better AI performance
    2. AI learns by finding patterns in examples
    3. Quality of descriptions matters just as much as quantity
    4. Real AI companies collect massive amounts of data (millions or billions of examples!)

    Critical Thinking Extension: "This is why AI companies want to collect data about you. The more data they have, the better their AI works. Is this good or bad? What are the tradeoffs?" (Preview for future lessons on AI ethics and privacy)

  6. Individual Reflection and Assessment (5 minutes)

    Individual Reflection Worksheet: Students complete independently to demonstrate understanding

    1. Define it: "What is machine learning in your own words? Use an example from today's activity."
    2. Explain why: "Why does AI need lots of training data? What happens with only a few examples vs. many examples?"
    3. Show your data: "Draw a simple bar graph showing how your group's AI improved from Round 1 to Round 2. Label the axes!"
    4. Real-world application: "Name three real-world applications that use machine learning. How do you think they were trained?"
    5. Personal connection: "If you were training an AI to recognize your face for phone unlock, what different examples would you give it? (Think: lighting, angles, facial expressions)"
    6. Reflection: "What was the most interesting thing you learned today about how AI systems learn?"

    Optional Extension - See Real ML in Action:

    If time and technology permit, demonstrate one of these real AI tools:

    • Quick, Draw! (quickdraw.withgoogle.com) - Google's AI tries to guess your doodles, learning from millions of drawings
    • Teachable Machine (teachablemachine.withgoogle.com) - Students can train an AI to recognize hand gestures or sounds in real-time
    • Thing Translator - Uses image recognition to identify objects and translate their names

    Exit Ticket: On a sticky note, write: "Machine learning is like..." (complete the analogy). Collect as students leave to assess understanding.

Assessment Strategies

Formative Assessment

  • Observation of group participation and execution of assigned roles during game play
  • Quality and specificity of trainer descriptions and pattern identification during training phases
  • AI student's ability to improve performance and learn from feedback between rounds
  • Data recorder's accuracy in tracking results and attention to detail
  • Verbal responses during class discussion demonstrating conceptual understanding
  • Questions asked during training phases showing engagement with material
  • Understanding demonstrated through reflection questions and exit ticket responses
  • Ability to connect game experience to real-world AI applications

Summative Assessment

  • Completed reflection worksheet with accurate, thoughtful responses to all questions
  • Correctly constructed bar graph showing Round 1 vs. Round 2 accuracy with proper labels
  • Clear, accurate written explanation of machine learning concept using lesson vocabulary
  • Identification of at least three real-world AI applications that use machine learning
  • Completion of data recording sheet with accurate calculations of improvement percentage
  • Optional: Design a training program for a novel AI application showing understanding of training principles

Success Criteria

Students demonstrate mastery when they:

  • Accurately explain that AI improves with more training data and examples
  • Successfully identify patterns across multiple training examples
  • Demonstrate understanding through accurate data collection and analysis
  • Connect game experience to at least two real AI systems they've encountered
  • Describe at least three real-world machine learning applications with reasonable accuracy
  • Show improved accuracy in Round 2 compared to Round 1 (demonstrating learning occurred)
  • Demonstrate effective collaboration and communication skills within assigned roles
  • Use lesson vocabulary correctly (training data, pattern, accuracy, machine learning)

Differentiation Strategies

For Advanced Learners:

  • Introduce the concept of overfitting: when AI memorizes training examples instead of learning generalizable patterns. Challenge students to explain why this is problematic.
  • Have groups create their own category sets with more subtle differences (e.g., different dog breeds, types of geometric shapes)
  • Research and present on different types of machine learning: supervised vs. unsupervised learning, reinforcement learning
  • Calculate more complex statistics: mean, median, mode of accuracy scores; standard deviation if appropriate for grade level
  • Design a completely new machine learning game with different challenges and assessment criteria
  • Explore edge cases: "What if training data is biased? What if categories overlap?" (preview of AI ethics)

For Struggling Learners:

  • Use only 2-3 categories instead of 4 to reduce cognitive load and simplify pattern recognition
  • Provide visual cue cards listing possible features for each category as reference
  • Allow AI students to keep eyes open initially, then progress to blindfold once comfortable with the game
  • Assign supportive group roles that match individual student strengths and comfort levels
  • Provide sentence frames for reflection: "Machine learning is when...", "AI gets better when...", "I learned that..."
  • Use simplified reflection worksheet with multiple choice options or fill-in-the-blank format
  • Pre-teach key vocabulary with visual supports before the lesson begins
  • Allow extended time for both training phases and assessment completion

For English Language Learners:

  • Pre-teach essential vocabulary with visual supports: training, pattern, accuracy, category, feature, describe, recognize
  • Provide visual cards showing category examples with clear labels in English and native language if possible
  • Pair with fluent English speakers as training partners who can model language use
  • Allow use of native language during initial training descriptions, then encourage English
  • Provide bilingual reflection worksheet or access to translation tools
  • Use gestures, physical demonstrations, and visual aids during all explanations
  • Create a word wall with lesson vocabulary, definitions, and example images
  • Provide extra processing time when asking questions or soliciting responses

For Students with Special Needs:

  • Provide noise-canceling headphones for students sensitive to group noise
  • Allow students uncomfortable with blindfolds to face away from trainers or use a screen divider instead
  • Modify activity for students with hearing impairments: use visual cards with written descriptions
  • For students with attention challenges: assign the active "AI" role to maintain engagement
  • Provide written instructions and visual schedules for each phase of the activity
  • Allow movement breaks between rounds for students who need kinesthetic activity
  • Offer alternative recording methods: verbal responses instead of written, typing instead of handwriting
  • Ensure all materials are in accessible formats (large print, high contrast, etc.)

Extension Activities

STEM Challenge - Create Your Own Training Set:

Students design their own categories and training cards for a new round of the game. Challenge questions to guide design: Can you create categories that are very similar (harder to distinguish) or very different (easier)? Can you create categories with overlapping features that might confuse the AI? Test your categories with another group!

Computer Science Connection - Algorithm Design:

Students write detailed step-by-step algorithms (instructions) for how their AI made decisions during the game. Use computational thinking to break down the process:

  1. Listen to description of new item
  2. Identify key features mentioned
  3. Compare features to known patterns from training
  4. Eliminate categories that don't match
  5. Make classification based on best match
  6. Receive feedback (correct/incorrect)
  7. Adjust internal understanding based on feedback

Challenge: Can you create a flowchart showing this decision-making process?

Math Integration - Advanced Data Analysis:

  • Calculate improvement percentages between rounds for each group and find class average
  • Create more detailed graphs showing category-specific accuracy (which categories had highest/lowest accuracy?)
  • Compare results across all class groups - identify patterns in which strategies led to best improvement
  • Make predictions: "If we did 3 more rounds with even more training data, what would the graph look like? Would improvement continue at the same rate?"
  • Explore the concept of diminishing returns graphically

Cross-Curricular Connection - Writing:

  • Science Connection: Compare AI learning to how humans and animals learn through experience and repetition
  • Social Studies Connection: Research how AI is used in different industries (healthcare, transportation, entertainment) and present findings
  • Language Arts: "Diary of an AI" - Creative writing from the perspective of an AI system being trained. Example: "Day 1: I saw my first cat today. Confusing! Day 5: Now I've seen 100 cats. I'm getting good at this! Day 10: I can even tell different cat breeds apart now!"

Real AI Exploration Projects:

  • Google's Teachable Machine: Have students train an actual AI to recognize hand gestures, sounds, or poses using their device camera and microphone. Observe how it improves with more examples!
  • Quick, Draw!: Play this Google AI game that learns to recognize doodles. Discuss: How many drawings do you think it was trained on? Why does it sometimes guess wrong?
  • AI Experiments: Explore the collection at experiments.withgoogle.com/collection/ai and choose one to investigate deeply
  • Emoji Scavenger Hunt: Use Google's machine learning game to find real-world objects that match emoji prompts

Research Project - Machine Learning in Action:

Students choose a real-world AI application and conduct research to answer:

  • What type of data does this AI system use for training?
  • Approximately how many examples does it need to work well?
  • How accurate is it? What's its error rate?
  • What happens when it makes mistakes? (Real examples)
  • How might the training data affect the AI's performance?
  • Present findings to the class with visual aids

Ethics and Society Discussion (Advanced):

Lead a structured debate or discussion on AI training data ethics:

  • What happens if training data is biased or incomplete?
  • If someone trains an AI on only one type of example (e.g., only faces of one ethnicity), what problems could arise?
  • Should companies be allowed to use your data to train AI without asking permission?
  • How can we make sure AI systems are trained fairly and work well for everyone?

These questions set up important conversations about AI ethics that can be explored in future lessons.

Long-term Project - Build a Class AI:

Over several weeks, the class collaboratively "trains" an AI system for a specific purpose:

  • Week 1: Choose an application (e.g., identifying types of leaves, recognizing emotions in drawings)
  • Week 2: Collect training data (photos, drawings, descriptions)
  • Week 3: Test the "AI" (classmates) and record accuracy
  • Week 4: Add more training data and retest - measure improvement
  • Week 5: Present results and reflections on the process

Teacher Notes and Tips

Common Misconceptions to Address:

  • Misconception: "AI can learn from just one or two examples, just like humans."
    Clarification: While humans can often learn from very few examples using prior knowledge, most AI systems need hundreds, thousands, or even millions of examples to learn patterns accurately. The game demonstrates this by showing improvement with more training data.
  • Misconception: "AI understands things the way humans do."
    Clarification: AI recognizes patterns in data but doesn't "understand" meaning the way we do. It's finding statistical correlations, not developing comprehension. The blindfolded AI demonstrates this - they're working with limited input, similar to how AI processes data.
  • Misconception: "More data is always better - there's no limit to improvement."
    Clarification: Introduce the concept of diminishing returns. While more data generally helps, after a certain point, adding more examples provides less and less improvement. Quality of data matters as much as quantity.
  • Misconception: "AI will always get perfect scores if trained enough."
    Clarification: No AI system is 100% accurate. There's always some error rate, especially with ambiguous or edge cases. This is why human oversight remains important.

Preparation Tips:

  • Print and laminate all training card sets for durability - one complete set per group (save these for future years!)
  • Color-code the four categories using different colored paper or markers on card backs for easy sorting
  • Prepare several example score sheets and graph templates to display as models before students begin
  • Test the blindfolds ahead of time to ensure they're comfortable, appropriate, and completely block vision
  • Create a demonstration set to model the training process with the whole class before group work begins
  • Prepare extension activity materials for groups that finish early (new category sets, research prompts, etc.)
  • Set up chart paper or whiteboard space for each group to post their results for class comparison
  • Have backup activities ready in case technology fails (if planning to show online AI demos)

Facilitating the Activity:

  • Demonstrate the training process with the whole class first: You be the AI, students collectively train you on 3-4 examples, then test with new items
  • Circulate constantly during group work to ensure all students are participating equitably in their roles
  • Listen for quality of descriptions: Encourage specificity ("has four wheels, engine, doors, and windows" not just "it's transportation")
  • Monitor the AI students: Are they asking good clarifying questions? Are they identifying patterns?
  • Pause the entire class briefly after Round 1 to share observations: "What's working well? What's challenging?"
  • Help struggling groups identify patterns they're missing: "Look at ALL the animals - what do they have in common?"
  • Manage time strictly: Use visible timers and give clear warnings ("2 minutes left for this phase")
  • Encourage positive team dynamics: Praise groups showing excellent collaboration

Classroom Management Strategies:

  • Establish clear voice level expectations: "Trainer voice should be heard only by your AI, not the whole room"
  • Create physical space between groups to minimize cross-contamination of training
  • Have a signal (raised hand, bell, lights) for getting whole class attention during group work
  • Assign specific roles to students who might be distracted - the Data Recorder role works well for detail-oriented students
  • Plan for smooth transitions between training and testing phases - have clear start/stop signals
  • Address students who finish early by having them create new categories or help struggling groups as consultants

Common Challenges and Solutions:

  • Challenge: AI student isn't improving between rounds
    Solution: Coach trainers to be more specific and consistent in descriptions. Ask: "Are you using the same words for the same features every time?" Help AI identify one key pattern at a time rather than trying to learn everything at once.
  • Challenge: Groups finish at very different times
    Solution: Have extension activities printed and ready. Fast-finishing groups can create new category sets, calculate additional statistics, or research real AI applications. Consider this variation in pacing when planning future lessons.
  • Challenge: Trainers are giving away answers instead of describing features
    Solution: Intervene immediately and redirect: "Remember, you're teaching patterns, not specific answers. The AI needs to learn HOW to categorize, not just memorize these exact items."
  • Challenge: Students uncomfortable with blindfolds
    Solution: Offer alternatives without stigma: facing away from the group, using a privacy screen or room divider, or simply closing eyes naturally. Never force the blindfold if a student is uncomfortable.
  • Challenge: Data recorder isn't staying engaged
    Solution: Emphasize importance of their role - they're the "scientists" collecting crucial data. Give them additional responsibilities like timing rounds, noting interesting observations, or creating the comparison graph.
  • Challenge: Groups arguing about whether answers were correct
    Solution: Teacher serves as final judge. Have answer keys ready. Use disagreements as teachable moments: "Even in real AI, sometimes the 'correct' answer isn't clear. How do AI designers handle this?"

Assessment Tips:

  • Observe ALL roles during the activity, not just the AI student - trainers' descriptions show understanding too
  • Take notes on groups demonstrating excellent pattern recognition or collaboration - share as examples
  • Note quality of pattern identification and description specificity - this shows depth of understanding
  • Check that data recorders are accurate and engaged throughout - their sheets are primary assessment artifacts
  • Listen carefully during class discussion for correct use of vocabulary and conceptual connections to real AI
  • Review reflection worksheets for conceptual understanding, not just completion - look for evidence of personal connections
  • Save sample student work (graphs, reflections, algorithms) as exemplars for future classes

Connection to Next Lesson:

This lesson sets up perfectly for multiple follow-up topics:

  • AI Bias and Fairness (Lesson 3): "What if we only trained the AI on one type of example? What if our training data was wrong or incomplete?"
  • Data Privacy: "We learned AI companies need lots of data. Where do they get it? Should they be allowed to use YOUR data?"
  • AI Ethics: "If an AI makes a mistake because of bad training data, who is responsible?"
  • Advanced ML Concepts: Supervised vs. unsupervised learning, neural networks, deep learning

Home Connection:

Send home a family engagement activity: "Find three examples of AI in your home or daily life. For each one, think about: What data was it trained on? How many examples do you think it needed? Can you tell when it makes mistakes?" This reinforces learning and involves families in AI education.

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