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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 127 · 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: “a drum beat for a dance” asks for an image. (a) True (b) False
  2. 2.📱 What is the AI doing here: a music app suggesting a song you may like? (a) recognising (b) recommending (c) predicting (d) generating
  3. 3.True or false: “flagging a new email” is training data. (a) False (b) True
  4. 4.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  5. 5.True or false: “checking labels twice” is good for training a model. (a) True (b) False
  6. 6.Which of these is a myth about AI? (a) AI answers need checking (b) AI can mix up numbers (c) AI can make up facts (d) AI always checks facts
  7. 7.Which of these is generative AI (it makes something new)? (a) creating a cartoon (b) spotting spam (c) unlocking with a face (d) predicting rain
  8. 8.🤔 An AI answer sounds very confident. Does that mean it is right? (a) no, check it anyway (b) yes, always (c) yes, if it is long
  9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
  10. 10.🧐 Fact or myth: “long answers are true”? (a) a fact (b) a myth
  11. 11.Which of these describes how AI works? (a) never learns from data (b) learns from examples (c) uses rules people wrote (d) follows fixed rules
  12. 12.🤖 What does AI stand for? (a) actual information (b) artificial intelligence (c) automatic internet (d) app installer
  13. 13.📚 An AI chatbot names a book that does not exist. This is called… (a) a hallucination (b) a summary (c) a backup (d) a password
  14. 14.Which of these describes how AI works? (a) same steps every time (b) does exactly as coded (c) one set rule per case (d) makes a best guess
  15. 15.True or false: counting the words in an essay only needs a simple fixed rule. (a) False (b) True
  16. 16.True or false: “handles unseen examples” describes how a normal program works. (a) True (b) False
  17. 17.True or false: “a thank-you note to the bus driver” asks for an image. (a) False (b) True
  18. 18.📱 What is the AI doing here: a phone unlocking when it sees your face? (a) generating (b) predicting (c) recognising (d) recommending
  19. 19.⏩ In machine learning, which comes first: the trained program or past weather records? (a) the trained program (b) past weather records
  20. 20.🧐 Fact or myth: “AI can be wrong”? (a) a fact (b) a myth
  21. 21.💬 What do we call the request you type to a generative AI? (a) a battery (b) a password (c) a label (d) a prompt
  22. 22.🏠 AI or not AI: an AI chatbot? (a) uses AI (b) not AI
  23. 23.Which of these is training data? (a) tomorrow’s rain guess (b) old emails marked spam (c) naming a new leaf (d) the learned patterns
  24. 24.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
  25. 25.True or false: sorting names in A to Z order only needs a simple fixed rule. (a) True (b) False
  26. 26.Which of these helps reduce bias? (a) use one group only (b) fix wrong labels (c) add more of the same (d) test on one group only
  27. 27.🎨 This prompt asks a generative AI for: a cheerful tune for Sports Day. What kind of output is that? (a) music or sound (b) an image (c) text
  28. 28.True or false: “what training builds” is training data. (a) True (b) False
  29. 29.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
  30. 30.⏩ In machine learning, which comes first: the trained program or labelled fruit photos? (a) the trained program (b) labelled fruit photos
  31. 31.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  32. 32.🧐 Fact or myth: “AI knows everything”? (a) a myth (b) a fact
  33. 33.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
  34. 34.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
  35. 35.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  36. 36.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) only one kind of example (d) clear, sharp examples
  37. 37.True or false: “correct labels” is bad for training a model. (a) True (b) False
  38. 38.🏠 AI or not AI: photos grouped by face? (a) not AI (b) uses AI
  39. 39.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  40. 40.True or false: “ask your teacher” is NOT a real way to check an AI answer. (a) True (b) False

Answer key

  1. False
  2. recommending
  3. False
  4. reduces bias
  5. True
  6. AI always checks facts
  7. creating a cartoon
  8. no, check it anyway
  9. not spam
  10. a myth
  11. learns from examples
  12. artificial intelligence
  13. a hallucination
  14. makes a best guess
  15. True
  16. False
  17. False
  18. recognising
  19. past weather records
  20. a fact
  21. a prompt
  22. uses AI
  23. old emails marked spam
  24. bad for training
  25. True
  26. fix wrong labels
  27. music or sound
  28. False
  29. bad for training
  30. labelled fruit photos
  31. balanced data
  32. a myth
  33. True
  34. True
  35. reduces bias
  36. only one kind of example
  37. False
  38. uses AI
  39. its training data
  40. False

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