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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 605 · 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 a prediction? (a) what training builds (b) labelled fruit photos (c) recorded spoken words (d) naming a new leaf
  2. 2.True or false: “testing on training data” is good for training a model. (a) False (b) True
  3. 3.True or false: showing the time on a clock needs AI that learns from examples. (a) True (b) False
  4. 4.True or false: an AI chatbot writing a poem about rain is AI generating something new. (a) True (b) False
  5. 5.True or false: “use one group only” helps reduce bias. (a) False (b) True
  6. 6.🔍 Which is the odd one out? (a) balance the data (b) copy old unfair choices (c) add varied examples (d) check who is missing
  7. 7.🧐 Fact or myth: “AI always checks facts”? (a) a myth (b) a fact
  8. 8.True or false: “one set rule per case” describes how AI works. (a) True (b) False
  9. 9.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) training data (c) the model
  10. 10.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  11. 11.Which of these adds to bias? (a) use one group only (b) add varied examples (c) ask different people (d) balance the data
  12. 12.True or false: “what training builds” is training data. (a) False (b) True
  13. 13.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
  14. 14.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
  15. 15.Which of these is a prediction? (a) flagging a new email (b) past weather records (c) old emails marked spam (d) what training builds
  16. 16.Which of these is training data? (a) what training builds (b) 500 labelled leaf photos (c) typing a new spoken word (d) naming a new fruit photo
  17. 17.🧐 Fact or myth: “AI is never wrong”? (a) a myth (b) a fact
  18. 18.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
  19. 19.🤖 AI or a normal program: which one “uses rules people wrote”? (a) a normal program (b) AI
  20. 20.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) test on many groups (d) ignore complaints
  21. 21.Which of these describes how a normal program works? (a) gives a likely answer (b) improves with more data (c) does exactly as coded (d) learns from examples
  22. 22.📱 What is the AI doing here: a shop guessing how many umbrellas will sell? (a) recognising (b) generating (c) predicting (d) recommending
  23. 23.Which of these is bad for training a model? (a) checking labels twice (b) wrong labels (c) many varied examples (d) examples of every type
  24. 24.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
  25. 25.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  26. 26.Which of these is bad for training a model? (a) labels added at random (b) checking labels twice (c) many varied examples (d) clear, sharp examples
  27. 27.✅ Is this a real check of an AI answer: ask your teacher? (a) not a real check (b) a real check
  28. 28.True or false: “learns from examples” describes how a normal program works. (a) True (b) False
  29. 29.🤖 AI or a normal program: which one “does exactly as coded”? (a) a normal program (b) AI
  30. 30.True or false: “tomorrow’s rain guess” is training data. (a) False (b) True
  31. 31.🧐 Fact or myth: “AI answers need checking”? (a) a fact (b) a myth
  32. 32.✨ Generative or not: reading number plates? (a) sorts or predicts (b) makes something new
  33. 33.True or false: “wrong labels” is bad for training a model. (a) True (b) False
  34. 34.True or false: “AI can make up facts” is a myth about AI. (a) True (b) False
  35. 35.🧐 Fact or myth: “AI knows everything”? (a) a myth (b) a fact
  36. 36.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) model, prediction, data (d) data, model, prediction
  37. 37.🛠️ Which does this job need: counting the words in an essay? (a) needs AI (learning) (b) a simple rule is enough
  38. 38.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
  39. 39.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
  40. 40.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen

Answer key

  1. naming a new leaf
  2. False
  3. False
  4. True
  5. False
  6. copy old unfair choices
  7. a myth
  8. False
  9. training data
  10. 500 labelled leaf photos
  11. use one group only
  12. False
  13. good for training
  14. False
  15. flagging a new email
  16. 500 labelled leaf photos
  17. a myth
  18. True
  19. a normal program
  20. ignore complaints
  21. does exactly as coded
  22. predicting
  23. wrong labels
  24. False
  25. training data
  26. labels added at random
  27. a real check
  28. False
  29. a normal program
  30. False
  31. a fact
  32. sorts or predicts
  33. True
  34. False
  35. a myth
  36. data, model, prediction
  37. a simple rule is enough
  38. many labelled photos
  39. AI
  40. bias from data

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