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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 73 · 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: “AI can make up facts” is a fact about AI. (a) False (b) True
  2. 2.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) training data (c) a prediction
  3. 3.🏠 AI or not AI: an app translating signs? (a) not AI (b) uses AI
  4. 4.✅ Is this a real check of an AI answer: it sounds very sure? (a) a real check (b) not a real check
  5. 5.True or false: “see if the book exists” is NOT a real way to check an AI answer. (a) True (b) False
  6. 6.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
  8. 8.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  9. 9.True or false: turning your spoken words into typed text is AI recognising something. (a) True (b) False
  10. 10.🧐 Fact or myth: “AI can be wrong”? (a) a myth (b) a fact
  11. 11.Which of these is training data? (a) naming a new leaf (b) naming a new fruit photo (c) flagging a new email (d) recorded spoken words
  12. 12.🔍 Which is the odd one out? (a) AI always checks facts (b) AI can make up facts (c) AI has real feelings (d) AI is never wrong
  13. 13.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  14. 14.True or false: translating a sentence into Hindi needs AI that learns from examples. (a) False (b) True
  15. 15.🛠️ Which does this job need: translating a sentence into Hindi? (a) needs AI (learning) (b) a simple rule is enough
  16. 16.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  17. 17.🔁 What is the right order? (a) data, model, prediction (b) prediction, data, model (c) model, prediction, data (d) data, prediction, model
  18. 18.🎨 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
  19. 19.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
  20. 20.True or false: recognising a friend’s face in photos needs AI that learns from examples. (a) False (b) True
  21. 21.🤖 AI or a normal program: which one “gives a likely answer”? (a) AI (b) a normal program
  22. 22.✅ Is this a real check of an AI answer: see if the book exists? (a) not a real check (b) a real check
  23. 23.Which of these is good for training a model? (a) many varied examples (b) only one kind of example (c) blurry, unclear photos (d) very few examples
  24. 24.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
  25. 25.Which of these adds to bias? (a) copy old unfair choices (b) check who is missing (c) add varied examples (d) fix wrong labels
  26. 26.✅ Is this a real check of an AI answer: find a trusted source? (a) a real check (b) not a real check
  27. 27.🔍 Which is the odd one out? (a) inventing a recipe (b) sorting photos by face (c) predicting rain (d) suggesting a video
  28. 28.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  29. 29.🤔 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
  30. 30.Which of these is a myth about AI? (a) AI answers need checking (b) AI knows everything (c) AI can mix up numbers (d) AI can make up facts
  31. 31.True or false: “handles unseen examples” describes how AI works. (a) False (b) True
  32. 32.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  33. 33.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) a prediction (c) the model
  34. 34.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
  35. 35.True or false: “use one group only” helps reduce bias. (a) True (b) False
  36. 36.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
  37. 37.🔍 Which is the odd one out? (a) a spam filter (b) a stopwatch (c) a digital watch (d) a torch
  38. 38.🛠️ Which does this job need: sorting names in A to Z order? (a) needs AI (learning) (b) a simple rule is enough
  39. 39.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  40. 40.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) add more of the same (d) use one group only

Answer key

  1. True
  2. the model
  3. uses AI
  4. not a real check
  5. False
  6. old emails marked spam
  7. spam
  8. a prediction
  9. True
  10. a fact
  11. recorded spoken words
  12. AI can make up facts
  13. reduces bias
  14. True
  15. needs AI (learning)
  16. True
  17. data, model, prediction
  18. text
  19. False
  20. True
  21. AI
  22. a real check
  23. many varied examples
  24. many labelled photos
  25. copy old unfair choices
  26. a real check
  27. inventing a recipe
  28. True
  29. no, check it anyway
  30. AI knows everything
  31. True
  32. False
  33. training data
  34. False
  35. False
  36. good for training
  37. a spam filter
  38. a simple rule is enough
  39. True
  40. add varied examples

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