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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 912 · 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.⏩ In machine learning, which comes first: the trained program or naming a new leaf? (a) the trained program (b) naming a new leaf
  2. 2.Which of these is generative AI (it makes something new)? (a) drawing a new picture (b) spotting a sick leaf (c) unlocking with a face (d) spotting spam
  3. 3.True or false: “it answered quickly” is NOT a real way to check an AI answer. (a) True (b) False
  4. 4.True or false: “five quiz questions on fractions” asks for text. (a) True (b) False
  5. 5.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  6. 6.📱 What is the AI doing here: turning your spoken words into typed text? (a) predicting (b) recommending (c) recognising (d) generating
  7. 7.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) test on one group only (d) ignore complaints
  8. 8.True or false: “what training builds” is the model. (a) True (b) False
  9. 9.🏠 AI or not AI: a doorbell? (a) not AI (b) uses AI
  10. 10.True or false: “labels added at random” is bad for training a model. (a) False (b) True
  11. 11.Which of these is not AI? (a) an app translating signs (b) an image generator (c) a stopwatch (d) face unlock on a phone
  12. 12.🎨 This prompt asks a generative AI for: a drum beat for a dance. What kind of output is that? (a) an image (b) music or sound (c) text
  13. 13.✅ Is this a real check of an AI answer: see if the book exists? (a) not a real check (b) a real check
  14. 14.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) needs AI (learning) (b) a simple rule is enough
  15. 15.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
  16. 16.⏩ In machine learning, which comes first: old emails marked spam or what training builds? (a) old emails marked spam (b) what training builds
  17. 17.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  18. 18.Which of these describes how a normal program works? (a) learns from examples (b) spots patterns in data (c) makes a best guess (d) same steps every time
  19. 19.🏷️ 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
  20. 20.✅ Is this a real check of an AI answer: redo the maths yourself? (a) a real check (b) not a real check
  21. 21.✨ Is everything a generative AI makes true? (a) yes, always (b) no, it can be wrong (c) only on Mondays
  22. 22.Which of these describes how a normal program works? (a) spots patterns in data (b) gives a likely answer (c) handles unseen examples (d) follows fixed rules
  23. 23.📅 An AI gives a date for a history event. How can you check? (a) ask it to repeat (b) pick a lucky number (c) guess (d) look in your textbook
  24. 24.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  25. 25.Which of these describes how a normal program works? (a) never learns from data (b) handles unseen examples (c) makes a best guess (d) learns from examples
  26. 26.True or false: switching on a street light at 6 pm needs AI that learns from examples. (a) True (b) False
  27. 27.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
  28. 28.True or false: “one set rule per case” describes how a normal program works. (a) False (b) True
  29. 29.True or false: “five quiz questions on fractions” asks for music or sound. (a) True (b) False
  30. 30.🤖 What does AI stand for? (a) actual information (b) artificial intelligence (c) app installer (d) automatic internet
  31. 31.🧮 Which of these is NOT AI? (a) face unlock (b) voice typing (c) a basic calculator (d) a spam filter
  32. 32.💬 What do we call the request you type to a generative AI? (a) a prompt (b) a battery (c) a label (d) a password
  33. 33.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  34. 34.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) skip testing (d) ask different people
  35. 35.🏠 AI or not AI: voice typing? (a) uses AI (b) not AI
  36. 36.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) training data (d) a password
  37. 37.🎨 This prompt asks a generative AI for: rain sound effects for a play. What kind of output is that? (a) an image (b) music or sound (c) text
  38. 38.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
  39. 39.🛠️ Which does this job need: adding up the marks on a report card? (a) a simple rule is enough (b) needs AI (learning)
  40. 40.Which of these describes how AI works? (a) spots patterns in data (b) never learns from data (c) one set rule per case (d) does exactly as coded

Answer key

  1. the trained program
  2. drawing a new picture
  3. True
  4. True
  5. a prediction
  6. recognising
  7. test on many groups
  8. True
  9. not AI
  10. True
  11. a stopwatch
  12. music or sound
  13. a real check
  14. needs AI (learning)
  15. bad for training
  16. old emails marked spam
  17. a prediction
  18. same steps every time
  19. not spam
  20. a real check
  21. no, it can be wrong
  22. follows fixed rules
  23. look in your textbook
  24. balanced data
  25. never learns from data
  26. False
  27. it may get it wrong
  28. True
  29. False
  30. artificial intelligence
  31. a basic calculator
  32. a prompt
  33. its training data
  34. ask different people
  35. uses AI
  36. a prediction
  37. music or sound
  38. bad for training
  39. a simple rule is enough
  40. spots patterns in data

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