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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 404 · 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 voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  2. 2.💬 What do we call the request you type to a generative AI? (a) a battery (b) a prompt (c) a label (d) a password
  3. 3.True or false: “labels added at random” is bad for training a model. (a) False (b) True
  4. 4.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it charges up (d) it prints labels
  5. 5.🛠️ Which does this job need: counting the words in an essay? (a) a simple rule is enough (b) needs AI (learning)
  6. 6.Which of these helps reduce bias? (a) ask different people (b) ignore complaints (c) add more of the same (d) skip testing
  7. 7.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
  9. 9.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) drawing (b) predicting (c) singing (d) sleeping
  10. 10.🧐 Fact or myth: “confident means correct”? (a) a fact (b) a myth
  11. 11.📺 A video app suggests what to watch next. What is the AI doing? (a) charging (b) printing (c) recommending (d) cooking
  12. 12.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) clear, sharp examples (d) many varied examples
  13. 13.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
  14. 14.📚 An AI chatbot names a book that does not exist. This is called… (a) a hallucination (b) a summary (c) a password (d) a backup
  15. 15.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
  16. 16.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
  17. 17.📱 What is the AI doing here: a map app guessing how long a trip will take? (a) predicting (b) recommending (c) recognising (d) generating
  18. 18.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
  19. 19.True or false: “learns from examples” describes how a normal program works. (a) True (b) False
  20. 20.🧐 Fact or myth: “AI answers need checking”? (a) a fact (b) a myth
  21. 21.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  22. 22.True or false: “never learns from data” describes how a normal program works. (a) False (b) True
  23. 23.📱 What is the AI doing here: a phone unlocking when it sees your face? (a) predicting (b) generating (c) recommending (d) recognising
  24. 24.True or false: “confident means correct” is a myth about AI. (a) False (b) True
  25. 25.True or false: “AI can be wrong” is a fact about AI. (a) False (b) True
  26. 26.🤖 AI or a normal program: which one “never learns from data”? (a) AI (b) a normal program
  27. 27.🔍 Which is the odd one out? (a) writing a new poem (b) making up a story (c) spotting a sick leaf (d) writing quiz questions
  28. 28.Which of these is generative AI (it makes something new)? (a) drawing a new picture (b) sorting photos by face (c) predicting rain (d) turning speech to text
  29. 29.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  30. 30.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) copy old unfair choices (d) add varied examples
  31. 31.True or false: “it has a neat layout” is NOT a real way to check an AI answer. (a) False (b) True
  32. 32.Which of these describes how AI works? (a) gives a likely answer (b) never learns from data (c) uses rules people wrote (d) does exactly as coded
  33. 33.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) clear, sharp examples (d) blurry, unclear photos
  34. 34.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
  35. 35.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  36. 36.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) check who is missing (d) use one group only
  37. 37.🛠️ Which does this job need: recognising a friend’s face in photos? (a) needs AI (learning) (b) a simple rule is enough
  38. 38.True or false: “find a trusted source” is a real way to check an AI answer. (a) True (b) False
  39. 39.True or false: “ask your teacher” is NOT a real way to check an AI answer. (a) True (b) False
  40. 40.Which of these is generative AI (it makes something new)? (a) creating a cartoon (b) predicting rain (c) spotting spam (d) unlocking with a face

Answer key

  1. False
  2. a prompt
  3. True
  4. it learns patterns
  5. a simple rule is enough
  6. ask different people
  7. one-sided data
  8. spam
  9. predicting
  10. a myth
  11. recommending
  12. wrong labels
  13. good for training
  14. a hallucination
  15. True
  16. good for training
  17. predicting
  18. a prediction
  19. False
  20. a fact
  21. True
  22. True
  23. recognising
  24. True
  25. True
  26. a normal program
  27. spotting a sick leaf
  28. drawing a new picture
  29. False
  30. copy old unfair choices
  31. True
  32. gives a likely answer
  33. clear, sharp examples
  34. bad for training
  35. its training data
  36. check who is missing
  37. needs AI (learning)
  38. True
  39. False
  40. creating a cartoon

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