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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 767 · 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: “what training builds” is training data. (a) False (b) True
  2. 2.True or false: “old emails marked spam” is the model. (a) True (b) False
  3. 3.🔍 Which is the odd one out? (a) writing a new poem (b) writing quiz questions (c) unlocking with a face (d) drafting a letter
  4. 4.🔍 Which is the odd one out? (a) AI always checks facts (b) AI answers need checking (c) long answers are true (d) AI knows everything
  5. 5.🤖 AI or a normal program: which one “does exactly as coded”? (a) AI (b) a normal program
  6. 6.✅ Is this a real check of an AI answer: match it to the textbook? (a) a real check (b) not a real check
  7. 7.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  8. 8.🏠 AI or not AI: a digital watch? (a) not AI (b) uses AI
  9. 9.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  10. 10.Which of these describes how a normal program works? (a) handles unseen examples (b) uses rules people wrote (c) gives a likely answer (d) learns from examples
  11. 11.🛠️ Which does this job need: ringing the school bell at 9 am? (a) a simple rule is enough (b) needs AI (learning)
  12. 12.🛠️ Which does this job need: counting the words in an essay? (a) needs AI (learning) (b) a simple rule is enough
  13. 13.Which of these is a fact about AI? (a) AI has real feelings (b) AI copies data patterns (c) AI knows everything (d) long answers are true
  14. 14.Which of these describes how AI works? (a) does exactly as coded (b) follows fixed rules (c) spots patterns in data (d) same steps every time
  15. 15.🤖 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
  16. 16.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
  17. 17.✅ Is this a real check of an AI answer: see if the book exists? (a) a real check (b) not a real check
  18. 18.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
  19. 19.🔍 Which is the odd one out? (a) AI learns from data (b) confident means correct (c) AI copies data patterns (d) AI can mix up numbers
  20. 20.Which of these is training data? (a) labelled fruit photos (b) what training builds (c) the learned patterns (d) naming a new fruit photo
  21. 21.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) check who is missing (d) use one group only
  22. 22.🏠 AI or not AI: a printed timetable? (a) uses AI (b) not AI
  23. 23.Which of these is a myth about AI? (a) AI copies data patterns (b) long answers are true (c) AI can mix up numbers (d) AI answers need checking
  24. 24.🔍 Which is the odd one out? (a) drafting a letter (b) composing a new tune (c) turning speech to text (d) creating a cartoon
  25. 25.🔦 Is a torch with an on/off switch AI? (a) no (b) only if it is bright (c) yes
  26. 26.True or false: “a cartoon of a dancing elephant” asks for music or sound. (a) True (b) False
  27. 27.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  28. 28.✨ Generative or not: predicting rain? (a) sorts or predicts (b) makes something new
  29. 29.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it will be perfect (d) it may get it wrong
  30. 30.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  31. 31.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  32. 32.🧮 Is a basic calculator an AI system? (a) only at night (b) yes, it can think (c) no, it follows rules (d) yes, it is smart
  33. 33.Which of these is generative AI (it makes something new)? (a) spotting a sick leaf (b) unlocking with a face (c) writing a new poem (d) suggesting a video
  34. 34.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
  35. 35.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
  36. 36.🔍 Which is the odd one out? (a) long answers are true (b) AI can make up facts (c) AI learns from data (d) AI can be wrong
  37. 37.Which of these helps reduce bias? (a) ignore complaints (b) ask different people (c) copy old unfair choices (d) test on one group only
  38. 38.🛠️ Which does this job need: finding 10% of a price? (a) a simple rule is enough (b) needs AI (learning)
  39. 39.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) a fair model (d) bias from data
  40. 40.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) add varied examples (d) use one group only

Answer key

  1. False
  2. False
  3. unlocking with a face
  4. AI answers need checking
  5. a normal program
  6. a real check
  7. reduces bias
  8. not AI
  9. True
  10. uses rules people wrote
  11. a simple rule is enough
  12. a simple rule is enough
  13. AI copies data patterns
  14. spots patterns in data
  15. no, it finds patterns
  16. few child voices in data
  17. a real check
  18. bad for training
  19. confident means correct
  20. labelled fruit photos
  21. use one group only
  22. not AI
  23. long answers are true
  24. turning speech to text
  25. no
  26. False
  27. True
  28. sorts or predicts
  29. it may get it wrong
  30. reduces bias
  31. balanced data
  32. no, it follows rules
  33. writing a new poem
  34. bad for training
  35. more varied data
  36. long answers are true
  37. ask different people
  38. a simple rule is enough
  39. bias from data
  40. add varied examples

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