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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 993 · 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) same steps every time (b) does exactly as coded (c) improves with more data (d) follows fixed rules
  2. 2.Which of these is the model? (a) typing a new spoken word (b) 500 labelled leaf photos (c) tomorrow’s rain guess (d) the learned patterns
  3. 3.🏠 AI or not AI: an image generator? (a) uses AI (b) not AI
  4. 4.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
  5. 5.True or false: a book app suggesting a story like your last one is AI generating something new. (a) False (b) True
  6. 6.Which of these is bad for training a model? (a) checking labels twice (b) examples of every type (c) only one kind of example (d) correct labels
  7. 7.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it charges up (d) it prints labels
  8. 8.🧮 Which of these is NOT AI? (a) a basic calculator (b) a spam filter (c) voice typing (d) face unlock
  9. 9.True or false: “a soft lullaby melody” asks for music or sound. (a) False (b) True
  10. 10.True or false: “tomorrow’s rain guess” is training data. (a) False (b) True
  11. 11.🧐 Fact or myth: “AI can make up facts”? (a) a myth (b) a fact
  12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
  13. 13.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) data, model, prediction (d) model, prediction, data
  14. 14.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) sorting photos by face (c) predicting rain (d) composing a new tune
  15. 15.True or false: a car camera reading a speed sign is AI recognising something. (a) False (b) True
  16. 16.True or false: “see if the book exists” is a real way to check an AI answer. (a) True (b) False
  17. 17.Which of these is not AI? (a) photos grouped by face (b) a torch (c) a map predicting traffic (d) a spam filter
  18. 18.✨ Generative or not: sorting photos by face? (a) makes something new (b) sorts or predicts
  19. 19.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  20. 20.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will be perfect (d) it will turn yellow
  21. 21.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.✅ Is this a real check of an AI answer: it answered quickly? (a) a real check (b) not a real check
  23. 23.💬 What do we call the request you type to a generative AI? (a) a battery (b) a password (c) a prompt (d) a label
  24. 24.🔍 Which is the odd one out? (a) a spam filter (b) face unlock on a phone (c) a fan regulator (d) a map predicting traffic
  25. 25.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
  26. 26.Which of these is training data? (a) typing a new spoken word (b) flagging a new email (c) the learned patterns (d) past weather records
  27. 27.🧐 Fact or myth: “AI is never wrong”? (a) a fact (b) a myth
  28. 28.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) new data for testing (d) wrong labels
  29. 29.🐶 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) a song about dogs (d) one single photo
  30. 30.True or false: an image generator drawing a robot from a sentence is AI generating something new. (a) True (b) False
  31. 31.🧐 Fact or myth: “AI answers need checking”? (a) a fact (b) a myth
  32. 32.True or false: turning spoken words into typed text only needs a simple fixed rule. (a) False (b) True
  33. 33.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  34. 34.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  35. 35.⏩ In machine learning, which comes first: the trained program or past weather records? (a) the trained program (b) past weather records
  36. 36.🎨 An image generator makes a picture from… (a) the weather (b) a text description (c) your password (d) a phone number
  37. 37.✨ Is everything a generative AI makes true? (a) no, it can be wrong (b) only on Mondays (c) yes, always
  38. 38.🔍 Which is the odd one out? (a) face unlock on a phone (b) a voice assistant (c) a spam filter (d) a light switch
  39. 39.📅 An AI gives a date for a history event. How can you check? (a) pick a lucky number (b) ask it to repeat (c) look in your textbook (d) guess
  40. 40.Which of these describes how AI works? (a) follows fixed rules (b) never learns from data (c) does exactly as coded (d) spots patterns in data

Answer key

  1. improves with more data
  2. the learned patterns
  3. uses AI
  4. good for training
  5. False
  6. only one kind of example
  7. it learns patterns
  8. a basic calculator
  9. True
  10. False
  11. a fact
  12. spam
  13. data, model, prediction
  14. composing a new tune
  15. True
  16. True
  17. a torch
  18. sorts or predicts
  19. True
  20. it may get it wrong
  21. True
  22. not a real check
  23. a prompt
  24. a fan regulator
  25. its training data
  26. past weather records
  27. a myth
  28. new data for testing
  29. many labelled photos
  30. True
  31. a fact
  32. False
  33. True
  34. the learned patterns
  35. past weather records
  36. a text description
  37. no, it can be wrong
  38. a light switch
  39. look in your textbook
  40. spots patterns in data

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