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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 619 · 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.True or false: “labelled fruit photos” is the model. (a) True (b) False
  2. 2.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) very few examples (d) examples of every type
  3. 3.🔍 Which is the odd one out? (a) an AI chatbot (b) a kitchen weighing scale (c) a torch (d) a stopwatch
  4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
  5. 5.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  6. 6.Which of these is a myth about AI? (a) AI can make up facts (b) AI is never wrong (c) AI can mix up numbers (d) AI copies data patterns
  7. 7.🔍 Which is the odd one out? (a) confident means correct (b) AI is never wrong (c) AI always checks facts (d) AI can mix up numbers
  8. 8.True or false: “match it to the textbook” is a real way to check an AI answer. (a) False (b) True
  9. 9.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  10. 10.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  11. 11.True or false: reading messy handwriting needs AI that learns from examples. (a) False (b) True
  12. 12.Which of these describes how AI works? (a) follows fixed rules (b) makes a best guess (c) same steps every time (d) never learns from data
  13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
  14. 14.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  15. 15.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  16. 16.True or false: a shop guessing how many umbrellas will sell is AI predicting something. (a) True (b) False
  17. 17.🤖 Does an AI system think and feel like a person? (a) yes, it gets sad (b) no, it finds patterns (c) yes, just like us (d) only on weekends
  18. 18.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  19. 19.Which of these is a fact about AI? (a) confident means correct (b) AI can be wrong (c) long answers are true (d) AI is never wrong
  20. 20.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
  22. 22.True or false: “recorded spoken words” is training data. (a) True (b) False
  23. 23.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) copy old unfair choices (d) ignore complaints
  24. 24.Which of these is a fact about AI? (a) long answers are true (b) AI always checks facts (c) AI is never wrong (d) AI learns from data
  25. 25.True or false: “add varied examples” adds to bias. (a) True (b) False
  26. 26.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  27. 27.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) prediction, data, model (d) data, model, prediction
  28. 28.Which of these is bad for training a model? (a) many varied examples (b) testing on training data (c) photos in many lights (d) correct labels
  29. 29.Which of these is the model? (a) recorded spoken words (b) labelled fruit photos (c) naming a new leaf (d) the learned patterns
  30. 30.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  31. 31.True or false: “test on one group only” adds to bias. (a) True (b) False
  32. 32.True or false: “a cartoon of a dancing elephant” asks for an image. (a) False (b) True
  33. 33.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  34. 34.True or false: “examples of every type” is good for training a model. (a) True (b) False
  35. 35.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
  36. 36.🧐 Fact or myth: “confident means correct”? (a) a fact (b) a myth
  37. 37.True or false: turning spoken words into typed text only needs a simple fixed rule. (a) False (b) True
  38. 38.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
  39. 39.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  40. 40.📱 What is the AI doing here: an AI tool making a brand-new tune? (a) generating (b) recommending (c) recognising (d) predicting

Answer key

  1. False
  2. very few examples
  3. an AI chatbot
  4. spam
  5. one-sided data
  6. AI is never wrong
  7. AI can mix up numbers
  8. True
  9. balanced data
  10. training data
  11. True
  12. makes a best guess
  13. not spam
  14. adds to bias
  15. True
  16. True
  17. no, it finds patterns
  18. False
  19. AI can be wrong
  20. one-sided data
  21. not spam
  22. True
  23. add varied examples
  24. AI learns from data
  25. False
  26. False
  27. data, model, prediction
  28. testing on training data
  29. the learned patterns
  30. 500 labelled leaf photos
  31. True
  32. True
  33. adds to bias
  34. True
  35. bad for training
  36. a myth
  37. False
  38. False
  39. reduces bias
  40. generating

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