AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 877 · 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.🔍 Which is the odd one out? (a) predicting rain (b) suggesting a video (c) unlocking with a face (d) creating a cartoon
2. 2.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
3. 3.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
4. 4.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) check who is missing (d) add more of the same
5. 5.True or false: a camera app naming the flower you point at is AI generating something new. (a) True (b) False
6. 6.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
7. 7.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
8. 8.✨ Generative or not: composing a new tune? (a) sorts or predicts (b) makes something new
9. 9.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
10. 10.Which of these uses AI? (a) a torch (b) a stopwatch (c) face unlock on a phone (d) a microwave timer
11. 11.🤖 AI or a normal program: which one “follows fixed rules”? (a) a normal program (b) AI
12. 12.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) use one group only (d) add varied examples
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) correct labels (d) examples of every type
14. 14.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) copies of one photo (d) photos in many lights
15. 15.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
16. 16.Which of these is a prediction? (a) the learned patterns (b) naming a new fruit photo (c) old emails marked spam (d) the trained program
17. 17.True or false: “a cheerful tune for Sports Day” asks for music or sound. (a) False (b) True
18. 18.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
19. 19.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
20. 20.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
21. 21.Which of these is good for training a model? (a) only one kind of example (b) new data for testing (c) copies of one photo (d) testing on training data
22. 22.Which of these describes how AI works? (a) does exactly as coded (b) learns from examples (c) one set rule per case (d) never learns from data
23. 23.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
24. 24.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
25. 25.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) the model (c) a prediction
26. 26.True or false: “very few examples” is good for training a model. (a) False (b) True
27. 27.✅ Is this a real check of an AI answer: find a trusted source? (a) not a real check (b) a real check
28. 28.True or false: “a picture of a castle made of mangoes” asks for text. (a) True (b) False
29. 29.🏷️ 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
30. 30.🧐 Fact or myth: “confident means correct”? (a) a myth (b) a fact
31. 31.True or false: “it is long and detailed” is a real way to check an AI answer. (a) False (b) True
32. 32.True or false: recognising a friend’s face in photos needs AI that learns from examples. (a) True (b) False
33. 33.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
34. 34.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) labels added at random (d) wrong labels
35. 35.🤖 AI or a normal program: which one “same steps every time”? (a) AI (b) a normal program
36. 36.📚 An AI chatbot names a book that does not exist. This is called… (a) a backup (b) a summary (c) a hallucination (d) a password
37. 37.True or false: “handles unseen examples” describes how a normal program works. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) AI can mix up numbers (b) AI has real feelings (c) AI copies data patterns (d) AI answers need checking
39. 39.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
40. 40.🧪 Why test a model on NEW examples? (a) to delete its data (b) to see if it works (c) to change its name (d) to make it slower

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