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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 853 · 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 does this job need: finding 10% of a price? (a) needs AI (learning) (b) a simple rule is enough
  2. 2.🎨 This prompt asks a generative AI for: write a poem about the monsoon. What kind of output is that? (a) text (b) music or sound (c) an image
  3. 3.🧐 Fact or myth: “AI can make up facts”? (a) a fact (b) a myth
  4. 4.Which of these is a fact about AI? (a) AI always checks facts (b) AI can make up facts (c) long answers are true (d) AI is never wrong
  5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
  6. 6.🔍 Which is the odd one out? (a) AI can be wrong (b) AI can mix up numbers (c) AI learns from data (d) AI knows everything
  7. 7.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  8. 8.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) balance the data (d) fix wrong labels
  9. 9.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
  10. 10.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
  11. 11.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  12. 12.🎨 This prompt asks a generative AI for: a drawing of a cat on the Moon. What kind of output is that? (a) an image (b) text (c) music or sound
  13. 13.🧐 Fact or myth: “long answers are true”? (a) a fact (b) a myth
  14. 14.🔍 Which is the odd one out? (a) new data for testing (b) only one kind of example (c) very few examples (d) labels added at random
  15. 15.True or false: “uses rules people wrote” describes how AI works. (a) True (b) False
  16. 16.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) new data for testing (d) examples of every type
  17. 17.✨ Generative or not: suggesting a video? (a) sorts or predicts (b) makes something new
  18. 18.✅ Is this a real check of an AI answer: ask your teacher? (a) a real check (b) not a real check
  19. 19.📺 A video app suggests what to watch next. What is the AI doing? (a) cooking (b) charging (c) printing (d) recommending
  20. 20.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
  21. 21.🔍 Which is the odd one out? (a) a torch (b) a microwave timer (c) photos grouped by face (d) a light switch
  22. 22.Which of these is a prediction? (a) labelled fruit photos (b) old emails marked spam (c) flagging a new email (d) 500 labelled leaf photos
  23. 23.Which of these describes how AI works? (a) follows fixed rules (b) never learns from data (c) improves with more data (d) uses rules people wrote
  24. 24.✨ Generative or not: reading number plates? (a) makes something new (b) sorts or predicts
  25. 25.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will be perfect (d) it will turn yellow
  26. 26.✅ Is this a real check of an AI answer: it is long and detailed? (a) not a real check (b) a real check
  27. 27.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) checking labels twice (d) testing on training data
  28. 28.True or false: “checking labels twice” is good for training a model. (a) False (b) True
  29. 29.True or false: “spots patterns in data” describes how AI works. (a) True (b) False
  30. 30.✨ Generative or not: sorting photos by face? (a) sorts or predicts (b) makes something new
  31. 31.Which of these is bad for training a model? (a) very few examples (b) many varied examples (c) checking labels twice (d) correct labels
  32. 32.🧮 Is a basic calculator an AI system? (a) yes, it is smart (b) only at night (c) yes, it can think (d) no, it follows rules
  33. 33.Which of these is a myth about AI? (a) AI copies data patterns (b) AI can make up facts (c) AI can mix up numbers (d) confident means correct
  34. 34.🤖 AI or a normal program: which one “handles unseen examples”? (a) a normal program (b) AI
  35. 35.🔍 Which is the odd one out? (a) confident means correct (b) AI learns from data (c) AI can be wrong (d) AI can make up facts
  36. 36.🔁 What is the right order? (a) data, model, prediction (b) model, prediction, data (c) prediction, data, model (d) data, prediction, model
  37. 37.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) singing (b) drawing (c) predicting (d) sleeping
  38. 38.Which of these describes how a normal program works? (a) improves with more data (b) makes a best guess (c) same steps every time (d) spots patterns in data
  39. 39.Which of these is training data? (a) flagging a new email (b) 500 labelled leaf photos (c) naming a new leaf (d) what training builds
  40. 40.🤖 AI or a normal program: which one “improves with more data”? (a) AI (b) a normal program

Answer key

  1. a simple rule is enough
  2. text
  3. a fact
  4. AI can make up facts
  5. not spam
  6. AI knows everything
  7. True
  8. skip testing
  9. False
  10. True
  11. reduces bias
  12. an image
  13. a myth
  14. new data for testing
  15. False
  16. labels added at random
  17. sorts or predicts
  18. a real check
  19. recommending
  20. AI
  21. photos grouped by face
  22. flagging a new email
  23. improves with more data
  24. sorts or predicts
  25. it may get it wrong
  26. not a real check
  27. checking labels twice
  28. True
  29. True
  30. sorts or predicts
  31. very few examples
  32. no, it follows rules
  33. confident means correct
  34. AI
  35. confident means correct
  36. data, model, prediction
  37. predicting
  38. same steps every time
  39. 500 labelled leaf photos
  40. AI

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