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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 391 · 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.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
  2. 2.Which of these is a prediction? (a) 500 labelled leaf photos (b) recorded spoken words (c) naming a new leaf (d) labelled fruit photos
  3. 3.Which of these uses AI? (a) a microwave timer (b) a stopwatch (c) a torch (d) a map predicting traffic
  4. 4.🎨 An image generator makes a picture from… (a) a phone number (b) a text description (c) the weather (d) your password
  5. 5.🎨 This prompt asks a generative AI for: a drawing of a cat on the Moon. What kind of output is that? (a) text (b) an image (c) music or sound
  6. 6.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) correct labels (d) only one kind of example
  7. 7.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  8. 8.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
  9. 9.True or false: “improves with more data” describes how a normal program works. (a) False (b) True
  10. 10.📱 What is the AI doing here: a camera app naming the flower you point at? (a) recommending (b) generating (c) predicting (d) recognising
  11. 11.🔍 Which is the odd one out? (a) face unlock on a phone (b) a map predicting traffic (c) a printed timetable (d) an image generator
  12. 12.🤖 What makes an AI system different from a normal program? (a) it uses electricity (b) it has a screen (c) it is always right (d) it learns from examples
  13. 13.🎨 This prompt asks a generative AI for: a cartoon of a dancing elephant. What kind of output is that? (a) an image (b) music or sound (c) text
  14. 14.📚 An AI chatbot names a book that does not exist. This is called… (a) a summary (b) a hallucination (c) a backup (d) a password
  15. 15.Which of these uses AI? (a) a calculator (b) face unlock on a phone (c) a doorbell (d) a stopwatch
  16. 16.Which of these describes how a normal program works? (a) learns from examples (b) spots patterns in data (c) does exactly as coded (d) gives a likely answer
  17. 17.🔍 Which is the odd one out? (a) suggesting a video (b) spotting a sick leaf (c) reading number plates (d) composing a new tune
  18. 18.Which of these uses AI? (a) a microwave timer (b) a kitchen weighing scale (c) an AI chatbot (d) a fan regulator
  19. 19.Which of these uses AI? (a) a microwave timer (b) a doorbell (c) a digital watch (d) a spam filter
  20. 20.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
  22. 22.🔍 Which is the odd one out? (a) photos in many lights (b) labels added at random (c) clear, sharp examples (d) new data for testing
  23. 23.✅ Is this a real check of an AI answer: it sounds very sure? (a) not a real check (b) a real check
  24. 24.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
  25. 25.🛠️ Which does this job need: recognising a bird from its song? (a) needs AI (learning) (b) a simple rule is enough
  26. 26.Which of these describes how AI works? (a) handles unseen examples (b) one set rule per case (c) uses rules people wrote (d) does exactly as coded
  27. 27.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
  28. 28.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) children speak too fast (d) the phone was new
  29. 29.🛠️ Which does this job need: converting metres into centimetres? (a) needs AI (learning) (b) a simple rule is enough
  30. 30.Which of these is a prediction? (a) what training builds (b) past weather records (c) the learned patterns (d) flagging a new email
  31. 31.True or false: an image generator drawing a robot from a sentence is AI generating something new. (a) True (b) False
  32. 32.🔍 Which is the odd one out? (a) photos grouped by face (b) a calculator (c) a microwave timer (d) a light switch
  33. 33.True or false: “naming a new leaf” is the model. (a) False (b) True
  34. 34.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
  36. 36.Which of these describes how a normal program works? (a) improves with more data (b) never learns from data (c) handles unseen examples (d) spots patterns in data
  37. 37.🤖 AI or a normal program: which one “gives a likely answer”? (a) AI (b) a normal program
  38. 38.Which of these helps reduce bias? (a) add more of the same (b) skip testing (c) ask different people (d) copy old unfair choices
  39. 39.🛠️ Which does this job need: spotting spam emails by their patterns? (a) a simple rule is enough (b) needs AI (learning)
  40. 40.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns

Answer key

  1. it may get it wrong
  2. naming a new leaf
  3. a map predicting traffic
  4. a text description
  5. an image
  6. only one kind of example
  7. True
  8. good for training
  9. False
  10. recognising
  11. a printed timetable
  12. it learns from examples
  13. an image
  14. a hallucination
  15. face unlock on a phone
  16. does exactly as coded
  17. composing a new tune
  18. an AI chatbot
  19. a spam filter
  20. True
  21. spam
  22. labels added at random
  23. not a real check
  24. AI
  25. needs AI (learning)
  26. handles unseen examples
  27. good for training
  28. few child voices in data
  29. a simple rule is enough
  30. flagging a new email
  31. True
  32. photos grouped by face
  33. False
  34. False
  35. spam
  36. never learns from data
  37. AI
  38. ask different people
  39. needs AI (learning)
  40. the learned patterns

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