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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 382 · 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: “check who is missing” helps reduce bias. (a) False (b) True
  2. 2.✨ Generative or not: turning speech to text? (a) makes something new (b) sorts or predicts
  3. 3.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
  4. 4.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) very few examples (d) testing on training data
  5. 5.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  6. 6.True or false: “it sounds very sure” is a real way to check an AI answer. (a) False (b) True
  7. 7.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  8. 8.🏠 AI or not AI: face unlock on a phone? (a) not AI (b) uses AI
  9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
  10. 10.True or false: “spots patterns in data” describes how AI works. (a) True (b) False
  11. 11.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  12. 12.Which of these is a myth about AI? (a) AI answers need checking (b) AI can make up facts (c) AI copies data patterns (d) AI always checks facts
  13. 13.🗣️ 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
  14. 14.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) recommending (c) charging (d) cooking
  15. 15.True or false: sorting names in A to Z order only needs a simple fixed rule. (a) True (b) False
  16. 16.True or false: finding 10% of a price only needs a simple fixed rule. (a) False (b) True
  17. 17.🤖 Who builds and trains AI systems? (a) nobody at all (b) people (c) the weather (d) the Moon
  18. 18.📱 What is the AI doing here: an AI tool making a brand-new tune? (a) predicting (b) recommending (c) recognising (d) generating
  19. 19.🔍 Which is the odd one out? (a) AI can mix up numbers (b) AI answers need checking (c) AI copies data patterns (d) AI always checks facts
  20. 20.🤖 Does an AI system think and feel like a person? (a) no, it finds patterns (b) yes, just like us (c) only on weekends (d) yes, it gets sad
  21. 21.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  22. 22.🎨 This prompt asks a generative AI for: five quiz questions on fractions. What kind of output is that? (a) text (b) music or sound (c) an image
  23. 23.Which of these uses AI? (a) a kitchen weighing scale (b) a calculator (c) a voice assistant (d) a microwave timer
  24. 24.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  25. 25.🧮 Which of these is NOT AI? (a) voice typing (b) a basic calculator (c) a spam filter (d) face unlock
  26. 26.🤖 What makes an AI system different from a normal program? (a) it has a screen (b) it learns from examples (c) it is always right (d) it uses electricity
  27. 27.✨ Is everything a generative AI makes true? (a) no, it can be wrong (b) only on Mondays (c) yes, always
  28. 28.Which of these is training data? (a) flagging a new email (b) labelled fruit photos (c) naming a new fruit photo (d) typing a new spoken word
  29. 29.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) training data (c) a label error (d) a prediction
  30. 30.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  31. 31.Which of these is AI that sorts, spots or predicts (not generative)? (a) spotting spam (b) writing a new poem (c) inventing a recipe (d) drawing a new picture
  32. 32.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
  33. 33.🔍 Which is the odd one out? (a) AI has real feelings (b) AI answers need checking (c) AI knows everything (d) AI always checks facts
  34. 34.True or false: suggesting the next word as you type only needs a simple fixed rule. (a) False (b) True
  35. 35.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
  36. 36.🔍 Which is the odd one out? (a) creating a cartoon (b) drafting a letter (c) making up a story (d) spotting spam
  37. 37.✨ Generative or not: writing quiz questions? (a) sorts or predicts (b) makes something new
  38. 38.True or false: “AI can be wrong” is a myth about AI. (a) False (b) True
  39. 39.Which of these is training data? (a) what training builds (b) the trained program (c) 500 labelled leaf photos (d) the learned patterns
  40. 40.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) test on one group only (d) add varied examples

Answer key

  1. True
  2. sorts or predicts
  3. AI
  4. examples of every type
  5. True
  6. False
  7. reduces bias
  8. uses AI
  9. not spam
  10. True
  11. reduces bias
  12. AI always checks facts
  13. few child voices in data
  14. recommending
  15. True
  16. True
  17. people
  18. generating
  19. AI always checks facts
  20. no, it finds patterns
  21. the trained program
  22. text
  23. a voice assistant
  24. True
  25. a basic calculator
  26. it learns from examples
  27. no, it can be wrong
  28. labelled fruit photos
  29. a prediction
  30. one-sided data
  31. spotting spam
  32. learn from data
  33. AI answers need checking
  34. False
  35. its training data
  36. spotting spam
  37. makes something new
  38. False
  39. 500 labelled leaf photos
  40. test on one group only

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