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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 905 · 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 of these is generative AI (it makes something new)? (a) writing a new poem (b) unlocking with a face (c) suggesting a video (d) turning speech to text
  2. 2.🏠 AI or not AI: a microwave timer? (a) uses AI (b) not AI
  3. 3.🧮 Which of these is NOT AI? (a) face unlock (b) voice typing (c) a basic calculator (d) a spam filter
  4. 4.🔍 Which is the odd one out? (a) voice typing (b) a stopwatch (c) a spam filter (d) an app translating signs
  5. 5.Which of these is bad for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) many varied examples (d) new data for testing
  6. 6.📱 What is the AI doing here: a weather app guessing tomorrow’s rain? (a) generating (b) recognising (c) predicting (d) recommending
  7. 7.Which of these is not AI? (a) a voice assistant (b) a kitchen weighing scale (c) voice typing (d) an app translating signs
  8. 8.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.Which of these describes how a normal program works? (a) gives a likely answer (b) learns from examples (c) follows fixed rules (d) improves with more data
  10. 10.✨ Generative or not: suggesting a video? (a) sorts or predicts (b) makes something new
  11. 11.True or false: “old emails marked spam” is the model. (a) True (b) False
  12. 12.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will shut down (d) it will turn yellow
  13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
  14. 14.True or false: “follows fixed rules” describes how AI works. (a) True (b) False
  15. 15.Which of these is bad for training a model? (a) testing on training data (b) new data for testing (c) correct labels (d) many varied examples
  16. 16.Which of these is a fact about AI? (a) AI is never wrong (b) long answers are true (c) AI has real feelings (d) AI learns from data
  17. 17.🧐 Fact or myth: “AI knows everything”? (a) a myth (b) a fact
  18. 18.🔍 Which is the odd one out? (a) wrong labels (b) testing on training data (c) blurry, unclear photos (d) clear, sharp examples
  19. 19.📱 What is the AI doing here: an AI chatbot writing a story about a lost kite? (a) recognising (b) predicting (c) recommending (d) generating
  20. 20.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
  21. 21.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
  22. 22.True or false: “confident means correct” is a fact about AI. (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  24. 24.🏠 AI or not AI: face unlock on a phone? (a) uses AI (b) not AI
  25. 25.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  26. 26.📦 What happens during training? (a) it charges up (b) it prints labels (c) it takes photos (d) it learns patterns
  27. 27.True or false: “use one group only” helps reduce bias. (a) True (b) False
  28. 28.🧐 Fact or myth: “AI learns from data”? (a) a myth (b) a fact
  29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
  30. 30.True or false: “never learns from data” describes how a normal program works. (a) True (b) False
  31. 31.🔍 Which is the odd one out? (a) copies of one photo (b) clear, sharp examples (c) examples of every type (d) checking labels twice
  32. 32.True or false: “what training builds” is training data. (a) False (b) True
  33. 33.True or false: a map app guessing how long a trip will take is AI predicting something. (a) True (b) False
  34. 34.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) correct labels (d) photos in many lights
  35. 35.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) the price (d) a sticker on a laptop
  36. 36.Which of these is a prediction? (a) the learned patterns (b) recorded spoken words (c) typing a new spoken word (d) 500 labelled leaf photos
  37. 37.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  38. 38.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
  39. 39.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  40. 40.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training

Answer key

  1. writing a new poem
  2. not AI
  3. a basic calculator
  4. a stopwatch
  5. blurry, unclear photos
  6. predicting
  7. a kitchen weighing scale
  8. False
  9. follows fixed rules
  10. sorts or predicts
  11. False
  12. it may get it wrong
  13. not spam
  14. False
  15. testing on training data
  16. AI learns from data
  17. a myth
  18. clear, sharp examples
  19. generating
  20. better at its task
  21. good for training
  22. False
  23. one-sided data
  24. uses AI
  25. False
  26. it learns patterns
  27. False
  28. a fact
  29. spam
  30. True
  31. copies of one photo
  32. False
  33. True
  34. wrong labels
  35. the right answer tag
  36. typing a new spoken word
  37. reduces bias
  38. False
  39. balanced data
  40. bad for training

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