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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 802 · 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.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
  2. 2.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
  3. 3.Which of these is a fact about AI? (a) long answers are true (b) AI always checks facts (c) confident means correct (d) AI can be wrong
  4. 4.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) checking labels twice (d) correct labels
  5. 5.✨ Is everything a generative AI makes true? (a) only on Mondays (b) yes, always (c) no, it can be wrong
  6. 6.✅ Is this a real check of an AI answer: match it to the textbook? (a) not a real check (b) a real check
  7. 7.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) training data (c) the model
  8. 8.🧮 Which of these is NOT AI? (a) face unlock (b) voice typing (c) a basic calculator (d) a spam filter
  9. 9.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  10. 10.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  11. 11.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a label error (c) a password (d) a prediction
  12. 12.Which of these describes how AI works? (a) never learns from data (b) gives a likely answer (c) uses rules people wrote (d) does exactly as coded
  13. 13.Which of these is a prediction? (a) the trained program (b) what training builds (c) typing a new spoken word (d) the learned patterns
  14. 14.🔍 Which is the odd one out? (a) a kitchen weighing scale (b) a torch (c) a doorbell (d) video suggestions
  15. 15.Which of these is a fact about AI? (a) AI can mix up numbers (b) AI always checks facts (c) AI has real feelings (d) AI knows everything
  16. 16.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
  17. 17.True or false: “a summary of chapter 3” asks for music or sound. (a) True (b) False
  18. 18.🏠 AI or not AI: an AI chatbot? (a) not AI (b) uses AI
  19. 19.True or false: “a drawing of a cat on the Moon” asks for music or sound. (a) False (b) True
  20. 20.🔍 Which is the odd one out? (a) AI can make up facts (b) AI learns from data (c) AI answers need checking (d) AI knows everything
  21. 21.🧐 Fact or myth: “AI answers need checking”? (a) a myth (b) a fact
  22. 22.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  23. 23.🧐 Fact or myth: “AI copies data patterns”? (a) a myth (b) a fact
  24. 24.True or false: recognising a friend’s face in photos needs AI that learns from examples. (a) False (b) True
  25. 25.Which of these is not AI? (a) a kitchen weighing scale (b) face unlock on a phone (c) an image generator (d) an app translating signs
  26. 26.🤖 What makes an AI system different from a normal program? (a) it learns from examples (b) it is always right (c) it has a screen (d) it uses electricity
  27. 27.True or false: counting the words in an essay needs AI that learns from examples. (a) True (b) False
  28. 28.True or false: “a picture of a castle made of mangoes” asks for an image. (a) True (b) False
  29. 29.True or false: “labels added at random” is good for training a model. (a) False (b) True
  30. 30.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) use one group only (d) fix wrong labels
  31. 31.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
  32. 32.Which of these is a prediction? (a) recorded spoken words (b) flagging a new email (c) old emails marked spam (d) 500 labelled leaf photos
  33. 33.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
  34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
  35. 35.True or false: “asking the same AI again” is NOT a real way to check an AI answer. (a) False (b) True
  36. 36.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  37. 37.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) yes, it gets sad (c) no, it finds patterns (d) only on weekends
  38. 38.True or false: recognising a bird from its song needs AI that learns from examples. (a) True (b) False
  39. 39.🧐 Fact or myth: “AI can mix up numbers”? (a) a myth (b) a fact
  40. 40.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price

Answer key

  1. spam
  2. False
  3. AI can be wrong
  4. only one kind of example
  5. no, it can be wrong
  6. a real check
  7. a prediction
  8. a basic calculator
  9. adds to bias
  10. True
  11. a prediction
  12. gives a likely answer
  13. typing a new spoken word
  14. video suggestions
  15. AI can mix up numbers
  16. bad for training
  17. False
  18. uses AI
  19. False
  20. AI knows everything
  21. a fact
  22. training data
  23. a fact
  24. True
  25. a kitchen weighing scale
  26. it learns from examples
  27. False
  28. True
  29. False
  30. fix wrong labels
  31. many labelled photos
  32. flagging a new email
  33. a myth
  34. not spam
  35. True
  36. reduces bias
  37. no, it finds patterns
  38. True
  39. a fact
  40. the right answer tag

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