AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 847 · 40 questions

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.

1. 1.True or false: “follows fixed rules” describes how a normal program works. (a) False (b) True
2. 2.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) prediction, data, model (d) model, prediction, data
3. 3.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
4. 4.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
5. 5.🧐 Fact or myth: “AI can be wrong”? (a) a myth (b) a fact
6. 6.🎨 An image generator makes a picture from… (a) the weather (b) your password (c) a phone number (d) a text description
7. 7.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) correct labels (d) only one kind of example
8. 8.Which of these is training data? (a) 500 labelled leaf photos (b) tomorrow’s rain guess (c) the trained program (d) what training builds
9. 9.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
10. 10.✨ Generative or not: writing quiz questions? (a) makes something new (b) sorts or predicts
11. 11.🛠️ Which does this job need: turning spoken words into typed text? (a) needs AI (learning) (b) a simple rule is enough
12. 12.⏩ In machine learning, which comes first: naming a new fruit photo or past weather records? (a) naming a new fruit photo (b) past weather records
13. 13.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
14. 14.True or false: “write a poem about the monsoon” asks for text. (a) True (b) False
15. 15.🔍 Which is the odd one out? (a) AI can mix up numbers (b) AI copies data patterns (c) AI has real feelings (d) AI can be wrong
16. 16.🤖 Does an AI system think and feel like a person? (a) no, it finds patterns (b) yes, it gets sad (c) only on weekends (d) yes, just like us
17. 17.📱 What is the AI doing here: an AI chatbot writing a story about a lost kite? (a) predicting (b) recommending (c) recognising (d) generating
18. 18.🔍 Which is the odd one out? (a) test on one group only (b) balance the data (c) fix wrong labels (d) check who is missing
19. 19.🔢 An AI chatbot gives a sum’s answer. What is the smart thing to do? (a) never do maths (b) ask it to hurry (c) copy it straight away (d) check it on paper
20. 20.✨ Generative or not: unlocking with a face? (a) sorts or predicts (b) makes something new
21. 21.🔍 Which is the odd one out? (a) AI knows everything (b) AI is never wrong (c) AI always checks facts (d) AI can be wrong
22. 22.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) predicting (b) sleeping (c) singing (d) drawing
23. 23.True or false: “asking the same AI again” is a real way to check an AI answer. (a) True (b) False
24. 24.✨ Generative or not: composing a new tune? (a) sorts or predicts (b) makes something new
25. 25.📺 A video app suggests what to watch next. What is the AI doing? (a) cooking (b) printing (c) charging (d) recommending
26. 26.Which of these is a myth about AI? (a) AI answers need checking (b) AI always checks facts (c) AI can make up facts (d) AI can be wrong
27. 27.🎨 This prompt asks a generative AI for: a summary of chapter 3. What kind of output is that? (a) music or sound (b) an image (c) text
28. 28.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
29. 29.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
30. 30.Which of these helps reduce bias? (a) ignore complaints (b) test on many groups (c) test on one group only (d) skip testing
31. 31.✅ Is this a real check of an AI answer: it uses big words? (a) a real check (b) not a real check
32. 32.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
33. 33.🔍 Which is the odd one out? (a) suggesting a video (b) inventing a recipe (c) spotting a sick leaf (d) reading number plates
34. 34.🏷️ 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) spam (b) not spam
35. 35.Which of these is not AI? (a) an AI chatbot (b) video suggestions (c) a spam filter (d) a calculator
36. 36.⏩ In machine learning, which comes first: the trained program or naming a new fruit photo? (a) the trained program (b) naming a new fruit photo
37. 37.🤖 Who builds and trains AI systems? (a) the weather (b) nobody at all (c) the Moon (d) people
38. 38.✨ Generative or not: turning speech to text? (a) sorts or predicts (b) makes something new
39. 39.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
40. 40.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) the model (c) a prediction

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