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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 659 · 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: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  2. 2.🔍 Which is the odd one out? (a) skip testing (b) add varied examples (c) add more of the same (d) copy old unfair choices
  3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) a strong battery (d) bias from data
  4. 4.True or false: “very few examples” is bad for training a model. (a) True (b) False
  5. 5.🔍 Which is the odd one out? (a) spotting a sick leaf (b) drafting a letter (c) reading number plates (d) spotting spam
  6. 6.🤖 AI or a normal program: which one “does exactly as coded”? (a) AI (b) a normal program
  7. 7.🗣️ 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) the phone was new (d) it was a rainy day
  8. 8.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
  9. 9.🛠️ Which does this job need: recognising a bird from its song? (a) needs AI (learning) (b) a simple rule is enough
  10. 10.✨ Generative or not: drawing a new picture? (a) sorts or predicts (b) makes something new
  11. 11.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
  12. 12.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) ignore complaints (d) ask different people
  13. 13.True or false: “a picture of a castle made of mangoes” asks for text. (a) True (b) False
  14. 14.Which of these uses AI? (a) a light switch (b) voice typing (c) a calculator (d) a digital watch
  15. 15.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  16. 16.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) testing on training data (d) correct labels
  17. 17.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) testing on training data (d) photos in many lights
  18. 18.📱 What is the AI doing here: a phone unlocking when it sees your face? (a) recommending (b) generating (c) predicting (d) recognising
  19. 19.🎨 This prompt asks a generative AI for: a thank-you note to the bus driver. What kind of output is that? (a) music or sound (b) text (c) an image
  20. 20.📚 An AI chatbot names a book that does not exist. This is called… (a) a summary (b) a password (c) a hallucination (d) a backup
  21. 21.🛠️ Which does this job need: converting metres into centimetres? (a) a simple rule is enough (b) needs AI (learning)
  22. 22.True or false: finding 10% of a price needs AI that learns from examples. (a) False (b) True
  23. 23.True or false: “asking the same AI again” is a real way to check an AI answer. (a) True (b) False
  24. 24.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  25. 25.Which of these is a fact about AI? (a) confident means correct (b) AI answers need checking (c) AI is never wrong (d) AI has real feelings
  26. 26.🏠 AI or not AI: video suggestions? (a) uses AI (b) not AI
  27. 27.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  28. 28.✨ Generative or not: suggesting a video? (a) sorts or predicts (b) makes something new
  29. 29.⏩ In machine learning, which comes first: the trained program or naming a new fruit photo? (a) the trained program (b) naming a new fruit photo
  30. 30.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  31. 31.True or false: “confident means correct” is a myth about AI. (a) True (b) False
  32. 32.📅 An AI gives a date for a history event. How can you check? (a) look in your textbook (b) pick a lucky number (c) guess (d) ask it to repeat
  33. 33.Which of these is generative AI (it makes something new)? (a) suggesting a video (b) writing a new poem (c) unlocking with a face (d) turning speech to text
  34. 34.Which of these describes how a normal program works? (a) learns from examples (b) spots patterns in data (c) gives a likely answer (d) never learns from data
  35. 35.True or false: “five quiz questions on fractions” asks for an image. (a) True (b) False
  36. 36.🤖 AI or a normal program: which one “improves with more data”? (a) AI (b) a normal program
  37. 37.True or false: turning spoken words into typed text only needs a simple fixed rule. (a) True (b) False
  38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
  39. 39.🔍 Which is the odd one out? (a) suggesting a video (b) making up a story (c) unlocking with a face (d) predicting rain
  40. 40.True or false: “ask your teacher” is a real way to check an AI answer. (a) False (b) True

Answer key

  1. True
  2. add varied examples
  3. bias from data
  4. True
  5. drafting a letter
  6. a normal program
  7. few child voices in data
  8. False
  9. needs AI (learning)
  10. makes something new
  11. True
  12. ask different people
  13. False
  14. voice typing
  15. one-sided data
  16. testing on training data
  17. photos in many lights
  18. recognising
  19. text
  20. a hallucination
  21. a simple rule is enough
  22. False
  23. False
  24. what training builds
  25. AI answers need checking
  26. uses AI
  27. balanced data
  28. sorts or predicts
  29. the trained program
  30. a prediction
  31. True
  32. look in your textbook
  33. writing a new poem
  34. never learns from data
  35. False
  36. AI
  37. False
  38. spam
  39. making up a story
  40. True

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