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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 676 · 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.🧮 Is a basic calculator an AI system? (a) yes, it is smart (b) no, it follows rules (c) yes, it can think (d) only at night
  2. 2.True or false: “uses rules people wrote” describes how AI works. (a) True (b) False
  3. 3.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
  4. 4.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) blurry, unclear photos (d) examples of every type
  5. 5.⏩ In machine learning, which comes first: old emails marked spam or flagging a new email? (a) old emails marked spam (b) flagging a new email
  6. 6.✨ Generative or not: inventing a recipe? (a) makes something new (b) sorts or predicts
  7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
  8. 8.Which of these is a myth about AI? (a) AI can be wrong (b) AI copies data patterns (c) long answers are true (d) AI learns from data
  9. 9.✅ Is this a real check of an AI answer: it has a neat layout? (a) a real check (b) not a real check
  10. 10.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  11. 11.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
  12. 12.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  13. 13.True or false: “a cheerful tune for Sports Day” asks for text. (a) True (b) False
  14. 14.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) the font size (d) a sticker on a laptop
  15. 15.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) ask different people (d) add more of the same
  16. 16.Which of these adds to bias? (a) ask different people (b) test on many groups (c) test on one group only (d) check who is missing
  17. 17.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) a list of rules only (d) many labelled photos
  18. 18.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  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) an image (c) text
  20. 20.🏠 AI or not AI: a stopwatch? (a) not AI (b) uses AI
  21. 21.True or false: “add varied examples” adds to bias. (a) False (b) True
  22. 22.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) the model (c) a prediction
  23. 23.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) only on weekends (c) yes, it gets sad (d) no, it finds patterns
  24. 24.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) training data (c) the model
  25. 25.True or false: “old emails marked spam” is training data. (a) False (b) True
  26. 26.🛠️ Which does this job need: turning spoken words into typed text? (a) a simple rule is enough (b) needs AI (learning)
  27. 27.Which of these is not AI? (a) voice typing (b) a spam filter (c) an AI chatbot (d) a digital watch
  28. 28.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  29. 29.📱 What is the AI doing here: a keyboard guessing your next word? (a) recognising (b) generating (c) recommending (d) predicting
  30. 30.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  31. 31.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
  32. 32.⏩ In machine learning, which comes first: tomorrow’s rain guess or the learned patterns? (a) tomorrow’s rain guess (b) the learned patterns
  33. 33.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  34. 34.🏠 AI or not AI: voice typing? (a) not AI (b) uses AI
  35. 35.🤖 AI or a normal program: which one “spots patterns in data”? (a) AI (b) a normal program
  36. 36.🔍 Which is the odd one out? (a) turning speech to text (b) drafting a letter (c) making up a story (d) creating a cartoon
  37. 37.True or false: “labels added at random” is good for training a model. (a) True (b) False
  38. 38.Which of these is training data? (a) the learned patterns (b) naming a new fruit photo (c) recorded spoken words (d) naming a new leaf
  39. 39.True or false: “labelled fruit photos” is training data. (a) False (b) True
  40. 40.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) test on many groups (d) skip testing

Answer key

  1. no, it follows rules
  2. False
  3. the model
  4. blurry, unclear photos
  5. old emails marked spam
  6. makes something new
  7. spam
  8. long answers are true
  9. not a real check
  10. one-sided data
  11. True
  12. True
  13. False
  14. the right answer tag
  15. add more of the same
  16. test on one group only
  17. many labelled photos
  18. reduces bias
  19. text
  20. not AI
  21. False
  22. training data
  23. no, it finds patterns
  24. the model
  25. True
  26. needs AI (learning)
  27. a digital watch
  28. reduces bias
  29. predicting
  30. one-sided data
  31. good for training
  32. the learned patterns
  33. True
  34. uses AI
  35. AI
  36. turning speech to text
  37. False
  38. recorded spoken words
  39. True
  40. skip testing

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