AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 38 · 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: “new data for testing” is good for training a model. (a) True (b) False
2. 2.🤔 An AI answer sounds very confident. Does that mean it is right? (a) yes, always (b) yes, if it is long (c) no, check it anyway
3. 3.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) a prediction (c) the model
4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a broken screen (d) a strong battery
5. 5.Which of these is a prediction? (a) old emails marked spam (b) what training builds (c) naming a new leaf (d) 500 labelled leaf photos
6. 6.True or false: “redo the maths yourself” is NOT a real way to check an AI answer. (a) True (b) False
7. 7.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) needs AI (learning) (b) a simple rule is enough
8. 8.🔍 Which is the odd one out? (a) reading number plates (b) inventing a recipe (c) unlocking with a face (d) sorting photos by face
9. 9.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) ask different people (d) add more of the same
10. 10.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
11. 11.🤖 What does AI stand for? (a) app installer (b) actual information (c) automatic internet (d) artificial intelligence
12. 12.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
13. 13.🎨 This prompt asks a generative AI for: write a poem about the monsoon. What kind of output is that? (a) an image (b) music or sound (c) text
14. 14.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
15. 15.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
16. 16.🔍 Which is the odd one out? (a) spotting spam (b) spotting a sick leaf (c) predicting rain (d) writing quiz questions
17. 17.🤖 What makes an AI system different from a normal program? (a) it uses electricity (b) it learns from examples (c) it is always right (d) it has a screen
18. 18.Which of these describes how AI works? (a) follows fixed rules (b) same steps every time (c) learns from examples (d) uses rules people wrote
19. 19.🧐 Fact or myth: “AI can be wrong”? (a) a myth (b) a fact
20. 20.✅ Is this a real check of an AI answer: see if the book exists? (a) a real check (b) not a real check
21. 21.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
22. 22.🧪 Why test a model on NEW examples? (a) to delete its data (b) to make it slower (c) to change its name (d) to see if it works
23. 23.True or false: “handles unseen examples” describes how AI works. (a) False (b) True
24. 24.True or false: “a drawing of a cat on the Moon” asks for text. (a) False (b) True
25. 25.Which of these describes how a normal program works? (a) improves with more data (b) uses rules people wrote (c) gives a likely answer (d) makes a best guess
26. 26.True or false: “very few examples” is good for training a model. (a) True (b) False
27. 27.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) use one group only (d) check who is missing
28. 28.True or false: “add varied examples” adds to bias. (a) False (b) True
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
30. 30.True or false: “ask different people” adds to bias. (a) False (b) True
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
32. 32.📺 A video app suggests what to watch next. What is the AI doing? (a) cooking (b) recommending (c) printing (d) charging
33. 33.True or false: “very few examples” is bad for training a model. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) spotting spam (b) unlocking with a face (c) suggesting a video (d) drawing a new picture
35. 35.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) many varied examples (d) very few examples
36. 36.True or false: “test on one group only” helps reduce bias. (a) True (b) False
37. 37.⏩ In machine learning, which comes first: the learned patterns or naming a new leaf? (a) the learned patterns (b) naming a new leaf
38. 38.True or false: a book app suggesting a story like your last one is AI recommending something. (a) True (b) False
39. 39.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
40. 40.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model

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