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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 248 · 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 good for training a model? (a) very few examples (b) labels added at random (c) blurry, unclear photos (d) clear, sharp examples
  2. 2.Which of these is bad for training a model? (a) very few examples (b) checking labels twice (c) examples of every type (d) photos in many lights
  3. 3.⏩ In machine learning, which comes first: labelled fruit photos or typing a new spoken word? (a) labelled fruit photos (b) typing a new spoken word
  4. 4.🏠 AI or not AI: a fan regulator? (a) not AI (b) uses AI
  5. 5.🔍 Which is the odd one out? (a) sorting photos by face (b) unlocking with a face (c) spotting a sick leaf (d) making up a story
  6. 6.True or false: “makes a best guess” describes how a normal program works. (a) True (b) False
  7. 7.🏠 AI or not AI: a digital watch? (a) not AI (b) uses AI
  8. 8.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
  9. 9.True or false: “recorded spoken words” is training data. (a) True (b) False
  10. 10.True or false: “ask your teacher” is a real way to check an AI answer. (a) True (b) False
  11. 11.Which of these is generative AI (it makes something new)? (a) sorting photos by face (b) turning speech to text (c) drawing a new picture (d) spotting spam
  12. 12.True or false: “confident means correct” is a fact about AI. (a) False (b) True
  13. 13.🎨 This prompt asks a generative AI for: a cheerful tune for Sports Day. What kind of output is that? (a) an image (b) text (c) music or sound
  14. 14.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  15. 15.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  16. 16.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) fix wrong labels (d) skip testing
  17. 17.True or false: an AI chatbot writing a story about a lost kite is AI predicting something. (a) True (b) False
  18. 18.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
  19. 19.Which of these is the model? (a) the learned patterns (b) naming a new fruit photo (c) past weather records (d) flagging a new email
  20. 20.True or false: turning your spoken words into typed text is AI recommending something. (a) True (b) False
  21. 21.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
  22. 22.🎨 This prompt asks a generative AI for: five quiz questions on fractions. What kind of output is that? (a) an image (b) text (c) music or sound
  23. 23.Which of these is a fact about AI? (a) AI always checks facts (b) AI is never wrong (c) AI learns from data (d) AI knows everything
  24. 24.True or false: a shop guessing how many umbrellas will sell is AI recommending something. (a) False (b) True
  25. 25.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  26. 26.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
  27. 27.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
  28. 28.📅 An AI gives a date for a history event. How can you check? (a) look in your textbook (b) pick a lucky number (c) ask it to repeat (d) guess
  29. 29.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (c) new data for testing (d) testing on training data
  30. 30.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) ignore complaints (d) add varied examples
  31. 31.Which of these describes how a normal program works? (a) same steps every time (b) spots patterns in data (c) handles unseen examples (d) improves with more data
  32. 32.Which of these uses AI? (a) a calculator (b) a stopwatch (c) an AI chatbot (d) a light switch
  33. 33.True or false: “ask different people” adds to bias. (a) False (b) True
  34. 34.🛠️ Which does this job need: spotting spam emails by their patterns? (a) a simple rule is enough (b) needs AI (learning)
  35. 35.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) many varied examples (d) testing on training data
  36. 36.🧐 Fact or myth: “AI can be wrong”? (a) a myth (b) a fact
  37. 37.Which of these uses AI? (a) a calculator (b) a printed timetable (c) a light switch (d) video suggestions
  38. 38.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) drafting a letter (c) sorting photos by face (d) reading number plates
  39. 39.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) balance the data (d) use one group only
  40. 40.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data

Answer key

  1. clear, sharp examples
  2. very few examples
  3. labelled fruit photos
  4. not AI
  5. making up a story
  6. False
  7. not AI
  8. its training data
  9. True
  10. True
  11. drawing a new picture
  12. False
  13. music or sound
  14. False
  15. True
  16. fix wrong labels
  17. False
  18. needs AI (learning)
  19. the learned patterns
  20. False
  21. True
  22. text
  23. AI learns from data
  24. False
  25. a prediction
  26. True
  27. few child voices in data
  28. look in your textbook
  29. new data for testing
  30. ignore complaints
  31. same steps every time
  32. an AI chatbot
  33. False
  34. needs AI (learning)
  35. testing on training data
  36. a fact
  37. video suggestions
  38. drafting a letter
  39. balance the data
  40. training data

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