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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 632 · 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.🧐 Fact or myth: “AI is never wrong”? (a) a fact (b) a myth
  2. 2.True or false: “what training builds” is the model. (a) False (b) True
  3. 3.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  4. 4.Which of these describes how AI works? (a) one set rule per case (b) does exactly as coded (c) never learns from data (d) spots patterns in data
  5. 5.🛠️ Which does this job need: suggesting the next word as you type? (a) a simple rule is enough (b) needs AI (learning)
  6. 6.✍️ How does an AI chatbot write its replies? (a) predicts likely words (b) uses magic (c) copies one website (d) asks a person
  7. 7.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) a prediction (c) training data
  8. 8.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
  9. 9.🛠️ Which does this job need: converting metres into centimetres? (a) a simple rule is enough (b) needs AI (learning)
  10. 10.Which of these describes how a normal program works? (a) same steps every time (b) handles unseen examples (c) learns from examples (d) makes a best guess
  11. 11.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  12. 12.🧐 Fact or myth: “AI can make up facts”? (a) a fact (b) a myth
  13. 13.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) bias from data (d) a strong battery
  14. 14.🔍 Which is the odd one out? (a) reading number plates (b) creating a cartoon (c) writing quiz questions (d) making up a story
  15. 15.🎨 This prompt asks a generative AI for: a cheerful tune for Sports Day. What kind of output is that? (a) text (b) music or sound (c) an image
  16. 16.📺 A video app suggests what to watch next. What is the AI doing? (a) cooking (b) printing (c) charging (d) recommending
  17. 17.🔍 Which is the odd one out? (a) a fan regulator (b) photos grouped by face (c) a voice assistant (d) a spam filter
  18. 18.🔍 Which is the odd one out? (a) photos in many lights (b) examples of every type (c) clear, sharp examples (d) labels added at random
  19. 19.Which of these is a fact about AI? (a) AI is never wrong (b) confident means correct (c) AI learns from data (d) AI has real feelings
  20. 20.🔍 Which is the odd one out? (a) AI answers need checking (b) AI can make up facts (c) AI has real feelings (d) AI can be wrong
  21. 21.✨ Generative or not: creating a cartoon? (a) sorts or predicts (b) makes something new
  22. 22.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) writing quiz questions (c) predicting rain (d) unlocking with a face
  23. 23.📅 An AI gives a date for a history event. How can you check? (a) guess (b) ask it to repeat (c) look in your textbook (d) pick a lucky number
  24. 24.Which of these describes how a normal program works? (a) spots patterns in data (b) improves with more data (c) one set rule per case (d) learns from examples
  25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
  26. 26.True or false: “a cartoon of a dancing elephant” asks for text. (a) False (b) True
  27. 27.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
  28. 28.True or false: finding 10% of a price only needs a simple fixed rule. (a) False (b) True
  29. 29.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it learns patterns (d) it charges up
  30. 30.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) model, prediction, data (d) data, model, prediction
  31. 31.✨ Generative or not: drawing a new picture? (a) makes something new (b) sorts or predicts
  32. 32.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) labels added at random (d) checking labels twice
  33. 33.✅ Is this a real check of an AI answer: it is long and detailed? (a) a real check (b) not a real check
  34. 34.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
  35. 35.True or false: a camera app naming the flower you point at is AI recognising something. (a) False (b) True
  36. 36.🤖 What makes an AI system different from a normal program? (a) it learns from examples (b) it has a screen (c) it uses electricity (d) it is always right
  37. 37.True or false: a phone unlocking when it sees your face is AI recommending something. (a) False (b) True
  38. 38.🎨 This prompt asks a generative AI for: a drawing of a cat on the Moon. What kind of output is that? (a) an image (b) text (c) music or sound
  39. 39.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  40. 40.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model

Answer key

  1. a myth
  2. True
  3. training data
  4. spots patterns in data
  5. needs AI (learning)
  6. predicts likely words
  7. a prediction
  8. bad for training
  9. a simple rule is enough
  10. same steps every time
  11. True
  12. a fact
  13. bias from data
  14. reading number plates
  15. music or sound
  16. recommending
  17. a fan regulator
  18. labels added at random
  19. AI learns from data
  20. AI has real feelings
  21. makes something new
  22. writing quiz questions
  23. look in your textbook
  24. one set rule per case
  25. spam
  26. False
  27. True
  28. True
  29. it learns patterns
  30. data, model, prediction
  31. makes something new
  32. checking labels twice
  33. not a real check
  34. AI
  35. True
  36. it learns from examples
  37. False
  38. an image
  39. adds to bias
  40. training data

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