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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 538 · 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.✅ Is this a real check of an AI answer: it answered quickly? (a) not a real check (b) a real check
  2. 2.Which of these helps reduce bias? (a) fix wrong labels (b) add more of the same (c) ignore complaints (d) test on one group only
  3. 3.✍️ How does an AI chatbot write its replies? (a) copies one website (b) uses magic (c) asks a person (d) predicts likely words
  4. 4.✅ Is this a real check of an AI answer: try the sum on paper? (a) not a real check (b) a real check
  5. 5.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
  6. 6.🎨 This prompt asks a generative AI for: a soft lullaby melody. What kind of output is that? (a) an image (b) music or sound (c) text
  7. 7.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) add more of the same (d) fix wrong labels
  8. 8.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  9. 9.True or false: “asking the same AI again” is NOT a real way to check an AI answer. (a) True (b) False
  10. 10.✨ Generative or not: reading number plates? (a) sorts or predicts (b) makes something new
  11. 11.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  12. 12.🧐 Fact or myth: “AI knows everything”? (a) a myth (b) a fact
  13. 13.🧮 Is a basic calculator an AI system? (a) yes, it can think (b) only at night (c) no, it follows rules (d) yes, it is smart
  14. 14.🔍 Which is the odd one out? (a) a microwave timer (b) a doorbell (c) a calculator (d) voice typing
  15. 15.🔍 Which is the odd one out? (a) a microwave timer (b) a digital watch (c) a torch (d) face unlock on a phone
  16. 16.📱 What is the AI doing here: a video app suggesting what to watch next? (a) recognising (b) recommending (c) generating (d) predicting
  17. 17.Which of these is bad for training a model? (a) blurry, unclear photos (b) checking labels twice (c) photos in many lights (d) many varied examples
  18. 18.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  19. 19.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  20. 20.Which of these describes how AI works? (a) never learns from data (b) uses rules people wrote (c) follows fixed rules (d) gives a likely answer
  21. 21.True or false: recognising a bird from its song needs AI that learns from examples. (a) True (b) False
  22. 22.Which of these describes how AI works? (a) uses rules people wrote (b) spots patterns in data (c) same steps every time (d) follows fixed rules
  23. 23.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) training data (c) a label error (d) a password
  24. 24.🔍 Which is the odd one out? (a) confident means correct (b) AI has real feelings (c) AI knows everything (d) AI can be wrong
  25. 25.Which of these is training data? (a) tomorrow’s rain guess (b) typing a new spoken word (c) 500 labelled leaf photos (d) the learned patterns
  26. 26.📦 What happens during training? (a) it learns patterns (b) it takes photos (c) it prints labels (d) it charges up
  27. 27.True or false: “naming a new leaf” is the model. (a) False (b) True
  28. 28.🔍 Which is the odd one out? (a) ignore complaints (b) test on one group only (c) check who is missing (d) use one group only
  29. 29.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
  30. 30.True or false: “a poster of a rainforest” asks for an image. (a) True (b) False
  31. 31.⏩ In machine learning, which comes first: what training builds or past weather records? (a) what training builds (b) past weather records
  32. 32.🔦 Is a torch with an on/off switch AI? (a) only if it is bright (b) yes (c) no
  33. 33.Which of these is training data? (a) what training builds (b) the learned patterns (c) typing a new spoken word (d) labelled fruit photos
  34. 34.🔍 Which is the odd one out? (a) face unlock on a phone (b) a spam filter (c) voice typing (d) a fan regulator
  35. 35.🔍 Which is the odd one out? (a) writing a new poem (b) spotting spam (c) inventing a recipe (d) creating a cartoon
  36. 36.True or false: “spots patterns in data” describes how AI works. (a) True (b) False
  37. 37.🏠 AI or not AI: a map predicting traffic? (a) uses AI (b) not AI
  38. 38.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) testing on training data (d) very few examples
  39. 39.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) labels added at random (d) testing on training data
  40. 40.Which of these is a prediction? (a) recorded spoken words (b) 500 labelled leaf photos (c) labelled fruit photos (d) naming a new leaf

Answer key

  1. not a real check
  2. fix wrong labels
  3. predicts likely words
  4. a real check
  5. AI
  6. music or sound
  7. add more of the same
  8. what training builds
  9. True
  10. sorts or predicts
  11. False
  12. a myth
  13. no, it follows rules
  14. voice typing
  15. face unlock on a phone
  16. recommending
  17. blurry, unclear photos
  18. a prediction
  19. 500 labelled leaf photos
  20. gives a likely answer
  21. True
  22. spots patterns in data
  23. a prediction
  24. AI can be wrong
  25. 500 labelled leaf photos
  26. it learns patterns
  27. False
  28. check who is missing
  29. learn from data
  30. True
  31. past weather records
  32. no
  33. labelled fruit photos
  34. a fan regulator
  35. spotting spam
  36. True
  37. uses AI
  38. examples of every type
  39. many varied examples
  40. naming a new leaf

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