← Part 1 test: what AI is lesson New set →

AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 904 · 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) what training builds (b) flagging a new email (c) the learned patterns (d) past weather records
  2. 2.True or false: “AI can mix up numbers” is a myth about AI. (a) False (b) True
  3. 3.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  4. 4.✨ Is everything a generative AI makes true? (a) yes, always (b) only on Mondays (c) no, it can be wrong
  5. 5.True or false: recognising a friend’s face in photos only needs a simple fixed rule. (a) False (b) True
  6. 6.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  7. 7.🔍 Which is the odd one out? (a) drafting a letter (b) spotting spam (c) writing a new poem (d) drawing a new picture
  8. 8.🔢 An AI chatbot gives a sum’s answer. What is the smart thing to do? (a) copy it straight away (b) never do maths (c) check it on paper (d) ask it to hurry
  9. 9.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
  10. 10.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a password (c) a label error (d) training data
  11. 11.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  12. 12.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) photos in many lights (d) blurry, unclear photos
  13. 13.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
  14. 14.True or false: “it is long and detailed” is NOT a real way to check an AI answer. (a) True (b) False
  15. 15.🔍 Which is the odd one out? (a) labels added at random (b) checking labels twice (c) correct labels (d) many varied examples
  16. 16.True or false: a book app suggesting a story like your last one is AI recommending something. (a) False (b) True
  17. 17.🔍 Which is the odd one out? (a) a digital watch (b) a fan regulator (c) a torch (d) an app translating signs
  18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
  19. 19.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) clear, sharp examples (d) labels added at random
  20. 20.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) ask different people (d) add more of the same
  21. 21.✅ Is this a real check of an AI answer: it uses big words? (a) not a real check (b) a real check
  22. 22.🎨 This prompt asks a generative AI for: five quiz questions on fractions. What kind of output is that? (a) an image (b) music or sound (c) text
  23. 23.✅ Is this a real check of an AI answer: try the sum on paper? (a) not a real check (b) a real check
  24. 24.Which of these is training data? (a) the trained program (b) naming a new fruit photo (c) typing a new spoken word (d) 500 labelled leaf photos
  25. 25.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it charges up (d) it prints labels
  26. 26.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) a strong battery (d) bias from data
  27. 27.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) balance the data (d) add varied examples
  28. 28.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) labels added at random (d) correct labels
  29. 29.Which of these is training data? (a) typing a new spoken word (b) what training builds (c) the learned patterns (d) recorded spoken words
  30. 30.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
  31. 31.🤖 AI or a normal program: which one “makes a best guess”? (a) a normal program (b) AI
  32. 32.🎨 This prompt asks a generative AI for: a poster of a rainforest. What kind of output is that? (a) music or sound (b) text (c) an image
  33. 33.📱 What is the AI doing here: a camera app naming the flower you point at? (a) generating (b) recommending (c) predicting (d) recognising
  34. 34.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  35. 35.🔍 Which is the odd one out? (a) confident means correct (b) AI can mix up numbers (c) AI is never wrong (d) AI always checks facts
  36. 36.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) cooking (c) charging (d) recommending
  37. 37.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
  38. 38.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  39. 39.True or false: “correct labels” is good for training a model. (a) True (b) False
  40. 40.True or false: a map app guessing how long a trip will take is AI predicting something. (a) True (b) False

Answer key

  1. past weather records
  2. False
  3. one-sided data
  4. no, it can be wrong
  5. False
  6. False
  7. spotting spam
  8. check it on paper
  9. the right answer tag
  10. a prediction
  11. False
  12. photos in many lights
  13. AI
  14. True
  15. labels added at random
  16. True
  17. an app translating signs
  18. not spam
  19. clear, sharp examples
  20. ask different people
  21. not a real check
  22. text
  23. a real check
  24. 500 labelled leaf photos
  25. it learns patterns
  26. bias from data
  27. skip testing
  28. correct labels
  29. recorded spoken words
  30. True
  31. AI
  32. an image
  33. recognising
  34. balanced data
  35. AI can mix up numbers
  36. recommending
  37. good for training
  38. True
  39. True
  40. True

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students