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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 648 · 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.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
  2. 2.🔍 Which is the odd one out? (a) writing a new poem (b) inventing a recipe (c) turning speech to text (d) drawing a new picture
  3. 3.✨ What does generative AI do? (a) only adds numbers (b) only stores files (c) charges batteries (d) makes new content
  4. 4.Which of these uses AI? (a) an AI chatbot (b) a microwave timer (c) a torch (d) a light switch
  5. 5.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) a simple rule is enough (b) needs AI (learning)
  6. 6.Which of these describes how AI works? (a) follows fixed rules (b) learns from examples (c) uses rules people wrote (d) does exactly as coded
  7. 7.⏩ In machine learning, which comes first: what training builds or flagging a new email? (a) what training builds (b) flagging a new email
  8. 8.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) cooking (c) recommending (d) charging
  9. 9.True or false: “photos in many lights” is good for training a model. (a) False (b) True
  10. 10.True or false: telling a cat photo from a dog photo needs AI that learns from examples. (a) False (b) True
  11. 11.🤖 What makes an AI system different from a normal program? (a) it uses electricity (b) it is always right (c) it has a screen (d) it learns from examples
  12. 12.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
  13. 13.True or false: “same steps every time” describes how AI works. (a) True (b) False
  14. 14.Which of these is generative AI (it makes something new)? (a) writing a new poem (b) sorting photos by face (c) spotting spam (d) suggesting a video
  15. 15.Which of these is a myth about AI? (a) AI copies data patterns (b) long answers are true (c) AI can be wrong (d) AI answers need checking
  16. 16.✨ Generative or not: writing a new poem? (a) sorts or predicts (b) makes something new
  17. 17.Which of these uses AI? (a) a kitchen weighing scale (b) a torch (c) photos grouped by face (d) a light switch
  18. 18.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) examples of every type (d) testing on training data
  19. 19.🛠️ Which does this job need: translating a sentence into Hindi? (a) a simple rule is enough (b) needs AI (learning)
  20. 20.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) a prediction (b) the model (c) training data
  21. 21.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) testing on training data (d) blurry, unclear photos
  22. 22.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  23. 23.🎨 This prompt asks a generative AI for: a drawing of a cat on the Moon. What kind of output is that? (a) text (b) music or sound (c) an image
  24. 24.True or false: “very few examples” is bad for training a model. (a) True (b) False
  25. 25.🥭 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
  26. 26.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  28. 28.🤔 An AI answer sounds very confident. Does that mean it is right? (a) yes, if it is long (b) yes, always (c) no, check it anyway
  29. 29.✨ Generative or not: composing a new tune? (a) makes something new (b) sorts or predicts
  30. 30.Which of these is AI that sorts, spots or predicts (not generative)? (a) creating a cartoon (b) writing quiz questions (c) unlocking with a face (d) composing a new tune
  31. 31.True or false: “one set rule per case” describes how AI works. (a) False (b) True
  32. 32.✅ Is this a real check of an AI answer: redo the maths yourself? (a) not a real check (b) a real check
  33. 33.True or false: “does exactly as coded” describes how a normal program works. (a) True (b) False
  34. 34.🔍 Which is the odd one out? (a) writing a new poem (b) inventing a recipe (c) sorting photos by face (d) writing quiz questions
  35. 35.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
  36. 36.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) balance the data (d) use one group only
  37. 37.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) test on many groups (d) test on one group only
  38. 38.🛠️ Which does this job need: spotting spam emails by their patterns? (a) needs AI (learning) (b) a simple rule is enough
  39. 39.✅ Is this a real check of an AI answer: ask your teacher? (a) not a real check (b) a real check
  40. 40.🛠️ Which does this job need: showing the time on a clock? (a) needs AI (learning) (b) a simple rule is enough

Answer key

  1. a myth
  2. turning speech to text
  3. makes new content
  4. an AI chatbot
  5. needs AI (learning)
  6. learns from examples
  7. what training builds
  8. recommending
  9. True
  10. True
  11. it learns from examples
  12. False
  13. False
  14. writing a new poem
  15. long answers are true
  16. makes something new
  17. photos grouped by face
  18. testing on training data
  19. needs AI (learning)
  20. a prediction
  21. examples of every type
  22. adds to bias
  23. an image
  24. True
  25. it may get it wrong
  26. False
  27. balanced data
  28. no, check it anyway
  29. makes something new
  30. unlocking with a face
  31. False
  32. a real check
  33. True
  34. sorting photos by face
  35. good for training
  36. balance the data
  37. test on one group only
  38. needs AI (learning)
  39. a real check
  40. a simple rule is enough

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