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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 752 · 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.Which of these is training data? (a) the learned patterns (b) the trained program (c) tomorrow’s rain guess (d) old emails marked spam
  2. 2.📺 A video app suggests what to watch next. What is the AI doing? (a) recommending (b) printing (c) cooking (d) charging
  3. 3.🔍 Which is the odd one out? (a) making up a story (b) writing a new poem (c) drafting a letter (d) spotting a sick leaf
  4. 4.🧮 Which of these is NOT AI? (a) face unlock (b) voice typing (c) a spam filter (d) a basic calculator
  5. 5.True or false: “a thank-you note to the bus driver” asks for text. (a) False (b) True
  6. 6.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
  7. 7.📦 What happens during training? (a) it charges up (b) it learns patterns (c) it prints labels (d) it takes photos
  8. 8.✨ Generative or not: spotting spam? (a) sorts or predicts (b) makes something new
  9. 9.🤖 What does AI stand for? (a) automatic internet (b) actual information (c) artificial intelligence (d) app installer
  10. 10.True or false: “improves with more data” describes how a normal program works. (a) False (b) True
  11. 11.🤖 AI or a normal program: which one “makes a best guess”? (a) a normal program (b) AI
  12. 12.🤖 AI or a normal program: which one “improves with more data”? (a) a normal program (b) AI
  13. 13.🧐 Fact or myth: “AI can make up facts”? (a) a fact (b) a myth
  14. 14.Which of these describes how a normal program works? (a) spots patterns in data (b) gives a likely answer (c) same steps every time (d) handles unseen examples
  15. 15.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  16. 16.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) checking labels twice (d) wrong labels
  17. 17.🔍 Which is the odd one out? (a) drawing a new picture (b) writing a new poem (c) predicting rain (d) composing a new tune
  18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
  19. 19.🧐 Fact or myth: “confident means correct”? (a) a fact (b) a myth
  20. 20.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  21. 21.🛠️ Which does this job need: turning spoken words into typed text? (a) a simple rule is enough (b) needs AI (learning)
  22. 22.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  23. 23.✅ Is this a real check of an AI answer: it has a neat layout? (a) not a real check (b) a real check
  24. 24.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
  25. 25.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  26. 26.🤔 An AI answer sounds very confident. Does that mean it is right? (a) yes, if it is long (b) yes, always (c) no, check it anyway
  27. 27.True or false: switching on a street light at 6 pm needs AI that learns from examples. (a) True (b) False
  28. 28.True or false: “improves with more data” describes how AI works. (a) False (b) True
  29. 29.✨ Generative or not: making up a story? (a) sorts or predicts (b) makes something new
  30. 30.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) clean screens (d) charge faster
  31. 31.True or false: “testing on training data” is bad for training a model. (a) False (b) True
  32. 32.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) blurry, unclear photos (d) examples of every type
  33. 33.Which of these is a fact about AI? (a) AI knows everything (b) AI always checks facts (c) AI copies data patterns (d) AI is never wrong
  34. 34.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
  35. 35.Which of these is not AI? (a) voice typing (b) a microwave timer (c) an image generator (d) face unlock on a phone
  36. 36.Which of these is bad for training a model? (a) correct labels (b) wrong labels (c) examples of every type (d) many varied examples
  37. 37.Which of these is training data? (a) typing a new spoken word (b) the trained program (c) 500 labelled leaf photos (d) what training builds
  38. 38.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  39. 39.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  40. 40.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen

Answer key

  1. old emails marked spam
  2. recommending
  3. spotting a sick leaf
  4. a basic calculator
  5. True
  6. False
  7. it learns patterns
  8. sorts or predicts
  9. artificial intelligence
  10. False
  11. AI
  12. AI
  13. a fact
  14. same steps every time
  15. adds to bias
  16. wrong labels
  17. predicting rain
  18. not spam
  19. a myth
  20. False
  21. needs AI (learning)
  22. a prediction
  23. not a real check
  24. more varied data
  25. balanced data
  26. no, check it anyway
  27. False
  28. True
  29. makes something new
  30. learn from data
  31. True
  32. blurry, unclear photos
  33. AI copies data patterns
  34. training data
  35. a microwave timer
  36. wrong labels
  37. 500 labelled leaf photos
  38. training data
  39. adds to bias
  40. bias from data

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