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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 621 · 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.🔍 Which is the odd one out? (a) drawing a new picture (b) creating a cartoon (c) predicting rain (d) composing a new tune
  2. 2.📅 An AI gives a date for a history event. How can you check? (a) pick a lucky number (b) ask it to repeat (c) guess (d) look in your textbook
  3. 3.📦 What happens during training? (a) it takes photos (b) it prints labels (c) it charges up (d) it learns patterns
  4. 4.True or false: “handles unseen examples” describes how AI works. (a) True (b) False
  5. 5.True or false: “ask your teacher” is NOT a real way to check an AI answer. (a) False (b) True
  6. 6.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) checking labels twice (d) many varied examples
  7. 7.🛠️ Which does this job need: translating a sentence into Hindi? (a) a simple rule is enough (b) needs AI (learning)
  8. 8.🔍 Which is the odd one out? (a) copies of one photo (b) correct labels (c) examples of every type (d) many varied examples
  9. 9.True or false: “photos in many lights” is good for training a model. (a) True (b) False
  10. 10.True or false: “learns from examples” describes how a normal program works. (a) True (b) False
  11. 11.🔍 Which is the odd one out? (a) writing quiz questions (b) spotting a sick leaf (c) reading number plates (d) unlocking with a face
  12. 12.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
  13. 13.🤖 Who builds and trains AI systems? (a) nobody at all (b) the weather (c) the Moon (d) people
  14. 14.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
  15. 15.🔍 Which is the odd one out? (a) composing a new tune (b) drawing a new picture (c) writing quiz questions (d) turning speech to text
  16. 16.Which of these is training data? (a) what training builds (b) the trained program (c) labelled fruit photos (d) flagging a new email
  17. 17.True or false: spotting spam emails by their patterns only needs a simple fixed rule. (a) True (b) False
  18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
  19. 19.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
  20. 20.🔍 Which is the odd one out? (a) AI learns from data (b) AI knows everything (c) AI has real feelings (d) confident means correct
  21. 21.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
  22. 22.Which of these is bad for training a model? (a) checking labels twice (b) labels added at random (c) examples of every type (d) correct labels
  23. 23.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  24. 24.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) yes, it gets sad (c) only on weekends (d) no, it finds patterns
  25. 25.Which of these is a myth about AI? (a) AI can be wrong (b) long answers are true (c) AI copies data patterns (d) AI answers need checking
  26. 26.🏠 AI or not AI: a light switch? (a) not AI (b) uses AI
  27. 27.⏩ In machine learning, which comes first: 500 labelled leaf photos or the learned patterns? (a) 500 labelled leaf photos (b) the learned patterns
  28. 28.Which of these is a myth about AI? (a) AI can be wrong (b) AI always checks facts (c) AI learns from data (d) AI can make up facts
  29. 29.🤖 AI or a normal program: which one “one set rule per case”? (a) AI (b) a normal program
  30. 30.Which of these describes how a normal program works? (a) learns from examples (b) handles unseen examples (c) one set rule per case (d) makes a best guess
  31. 31.🧮 Is a basic calculator an AI system? (a) yes, it is smart (b) yes, it can think (c) only at night (d) no, it follows rules
  32. 32.🔍 Which is the odd one out? (a) a fan regulator (b) photos grouped by face (c) a doorbell (d) a digital watch
  33. 33.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) wrong labels (d) blurry, unclear photos
  34. 34.🔍 Which is the odd one out? (a) drafting a letter (b) creating a cartoon (c) writing a new poem (d) unlocking with a face
  35. 35.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
  36. 36.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) labels added at random (d) examples of every type
  37. 37.True or false: a shop guessing how many umbrellas will sell is AI recognising something. (a) True (b) False
  38. 38.True or false: “use one group only” helps reduce bias. (a) False (b) True
  39. 39.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) new data for testing (d) very few examples

Answer key

  1. predicting rain
  2. look in your textbook
  3. it learns patterns
  4. True
  5. False
  6. labels added at random
  7. needs AI (learning)
  8. copies of one photo
  9. True
  10. False
  11. writing quiz questions
  12. False
  13. people
  14. bad for training
  15. turning speech to text
  16. labelled fruit photos
  17. False
  18. not spam
  19. bad for training
  20. AI learns from data
  21. AI
  22. labels added at random
  23. False
  24. no, it finds patterns
  25. long answers are true
  26. not AI
  27. 500 labelled leaf photos
  28. AI always checks facts
  29. a normal program
  30. one set rule per case
  31. no, it follows rules
  32. photos grouped by face
  33. new data for testing
  34. unlocking with a face
  35. good for training
  36. examples of every type
  37. False
  38. False
  39. False
  40. new data for testing

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