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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 8 · 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 describes how AI works? (a) never learns from data (b) follows fixed rules (c) learns from examples (d) one set rule per case
  2. 2.🤖 AI or a normal program: which one “improves with more data”? (a) AI (b) a normal program
  3. 3.True or false: “a thank-you note to the bus driver” asks for text. (a) True (b) False
  4. 4.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) a prediction (c) the model
  5. 5.True or false: “find a trusted source” is NOT a real way to check an AI answer. (a) True (b) False
  6. 6.Which of these is training data? (a) naming a new leaf (b) the learned patterns (c) typing a new spoken word (d) old emails marked spam
  7. 7.Which of these describes how a normal program works? (a) does exactly as coded (b) gives a likely answer (c) learns from examples (d) handles unseen examples
  8. 8.🎨 This prompt asks a generative AI for: rain sound effects for a play. What kind of output is that? (a) an image (b) music or sound (c) text
  9. 9.Which of these adds to bias? (a) add varied examples (b) add more of the same (c) ask different people (d) test on many groups
  10. 10.🎨 This prompt asks a generative AI for: five quiz questions on fractions. What kind of output is that? (a) text (b) an image (c) music or sound
  11. 11.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
  12. 12.Which of these is bad for training a model? (a) new data for testing (b) labels added at random (c) clear, sharp examples (d) examples of every type
  13. 13.True or false: “AI knows everything” is a fact about AI. (a) True (b) False
  14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) not spam (b) spam
  15. 15.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  16. 16.🔍 Which is the odd one out? (a) writing a new poem (b) writing quiz questions (c) inventing a recipe (d) turning speech to text
  17. 17.🤖 AI or a normal program: which one “makes a best guess”? (a) AI (b) a normal program
  18. 18.True or false: “examples of every type” is good for training a model. (a) True (b) False
  19. 19.True or false: “never learns from data” describes how AI works. (a) True (b) False
  20. 20.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a password (c) a label error (d) a prediction
  21. 21.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) a prediction (c) training data
  22. 22.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) blurry, unclear photos (d) checking labels twice
  23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
  24. 24.🤖 Who builds and trains AI systems? (a) the weather (b) nobody at all (c) people (d) the Moon
  25. 25.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
  26. 26.Which of these is good for training a model? (a) checking labels twice (b) very few examples (c) labels added at random (d) testing on training data
  27. 27.True or false: “gives a likely answer” describes how AI works. (a) False (b) True
  28. 28.True or false: “long answers are true” is a fact about AI. (a) False (b) True
  29. 29.🔍 Which is the odd one out? (a) sorting photos by face (b) writing a new poem (c) spotting a sick leaf (d) predicting rain
  30. 30.✅ Is this a real check of an AI answer: find a trusted source? (a) not a real check (b) a real check
  31. 31.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) drawing (b) singing (c) predicting (d) sleeping
  32. 32.🛠️ Which does this job need: reading messy handwriting? (a) a simple rule is enough (b) needs AI (learning)
  33. 33.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) fix wrong labels (d) copy old unfair choices
  34. 34.🤖 What does AI stand for? (a) actual information (b) automatic internet (c) app installer (d) artificial intelligence
  35. 35.🧐 Fact or myth: “AI can be wrong”? (a) a myth (b) a fact
  36. 36.⏩ In machine learning, which comes first: tomorrow’s rain guess or the trained program? (a) tomorrow’s rain guess (b) the trained program
  37. 37.True or false: “five quiz questions on fractions” asks for text. (a) True (b) False
  38. 38.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) checking labels twice (d) only one kind of example
  39. 39.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it charges up (d) it learns patterns
  40. 40.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) wrong labels (d) many varied examples

Answer key

  1. learns from examples
  2. AI
  3. True
  4. a prediction
  5. False
  6. old emails marked spam
  7. does exactly as coded
  8. music or sound
  9. add more of the same
  10. text
  11. True
  12. labels added at random
  13. False
  14. not spam
  15. balanced data
  16. turning speech to text
  17. AI
  18. True
  19. False
  20. a prediction
  21. the model
  22. blurry, unclear photos
  23. spam
  24. people
  25. bad for training
  26. checking labels twice
  27. True
  28. False
  29. writing a new poem
  30. a real check
  31. predicting
  32. needs AI (learning)
  33. copy old unfair choices
  34. artificial intelligence
  35. a fact
  36. the trained program
  37. True
  38. only one kind of example
  39. it learns patterns
  40. many varied examples

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