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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 204 · 40 questions
TalentJR

AI tip (Name the idea, then answer): Learning from examples → AI; fixed rules → a normal program. Order: training data → model → prediction; good data is many, varied and correctly labelled. Generative AI makes new things and can hallucinate; one-sided data causes bias.

NameDateTime takenScore ___ / 40
  1. 1.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
  2. 2.Which of these is bad for training a model? (a) checking labels twice (b) copies of one photo (c) clear, sharp examples (d) many varied examples
  3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
  4. 4.True or false: “what training builds” is a prediction. (a) False (b) True
  5. 5.🤖 AI or a normal program: which one “follows fixed rules”? (a) AI (b) a normal program
  6. 6.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  7. 7.🏠 AI or not AI: face unlock on a phone? (a) not AI (b) uses AI
  8. 8.True or false: “labelled fruit photos” is a prediction. (a) True (b) False
  9. 9.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) only on weekends (c) no, it finds patterns (d) yes, it gets sad
  10. 10.🔍 Which is the odd one out? (a) only one kind of example (b) photos in many lights (c) many varied examples (d) checking labels twice
  11. 11.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) training data (c) a prediction
  12. 12.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  13. 13.🏠 AI or not AI: a calculator? (a) uses AI (b) not AI
  14. 14.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  15. 15.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
  16. 16.📱 What is the AI doing here: a book app suggesting a story like your last one? (a) recommending (b) predicting (c) recognising (d) generating
  17. 17.True or false: suggesting the next word as you type only needs a simple fixed rule. (a) False (b) True
  18. 18.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  20. 20.🛠️ Which does this job need: recognising a bird from its song? (a) a simple rule is enough (b) needs AI (learning)
  21. 21.🧐 Fact or myth: “AI learns from data”? (a) a myth (b) a fact
  22. 22.🔍 Which is the odd one out? (a) a torch (b) a kitchen weighing scale (c) an image generator (d) a light switch
  23. 23.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  24. 24.⏩ In machine learning, which comes first: the learned patterns or naming a new fruit photo? (a) the learned patterns (b) naming a new fruit photo
  25. 25.True or false: “new data for testing” is good for training a model. (a) False (b) True
  26. 26.🧐 Fact or myth: “confident means correct”? (a) a fact (b) a myth
  27. 27.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) fix wrong labels (d) ignore complaints
  28. 28.True or false: “AI can mix up numbers” is a myth about AI. (a) False (b) True
  29. 29.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  30. 30.Which of these is bad for training a model? (a) correct labels (b) only one kind of example (c) checking labels twice (d) photos in many lights
  31. 31.⏩ In machine learning, which comes first: the learned patterns or naming a new leaf? (a) the learned patterns (b) naming a new leaf
  32. 32.True or false: “AI always checks facts” is a myth about AI. (a) True (b) False
  33. 33.True or false: “AI copies data patterns” is a myth about AI. (a) True (b) False
  34. 34.Which of these describes how AI works? (a) uses rules people wrote (b) spots patterns in data (c) follows fixed rules (d) does exactly as coded
  35. 35.🔍 Which is the odd one out? (a) only one kind of example (b) copies of one photo (c) testing on training data (d) photos in many lights
  36. 36.✨ Is everything a generative AI makes true? (a) no, it can be wrong (b) only on Mondays (c) yes, always
  37. 37.True or false: “it sounds very sure” is a real way to check an AI answer. (a) True (b) False
  38. 38.Which of these adds to bias? (a) use one group only (b) fix wrong labels (c) test on many groups (d) check who is missing
  39. 39.⏩ In machine learning, which comes first: naming a new fruit photo or the trained program? (a) naming a new fruit photo (b) the trained program
  40. 40.🤖 What does AI stand for? (a) app installer (b) artificial intelligence (c) actual information (d) automatic internet

Answer key

  1. bad for training
  2. copies of one photo
  3. spam
  4. False
  5. a normal program
  6. balanced data
  7. uses AI
  8. False
  9. no, it finds patterns
  10. only one kind of example
  11. training data
  12. reduces bias
  13. not AI
  14. True
  15. good for training
  16. recommending
  17. False
  18. True
  19. balanced data
  20. needs AI (learning)
  21. a fact
  22. an image generator
  23. one-sided data
  24. the learned patterns
  25. True
  26. a myth
  27. ignore complaints
  28. False
  29. training data
  30. only one kind of example
  31. the learned patterns
  32. True
  33. False
  34. spots patterns in data
  35. photos in many lights
  36. no, it can be wrong
  37. False
  38. use one group only
  39. the trained program
  40. artificial intelligence

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