AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 317 · 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.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
2. 2.Which of these is the model? (a) typing a new spoken word (b) what training builds (c) past weather records (d) flagging a new email
3. 3.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
4. 4.✨ Generative or not: spotting spam? (a) sorts or predicts (b) makes something new
5. 5.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
6. 6.True or false: “labels added at random” is good for training a model. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) ignore complaints (b) balance the data (c) use one group only (d) skip testing
8. 8.🏷️ 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) not spam (b) spam
9. 9.True or false: “many varied examples” is bad for training a model. (a) True (b) False
10. 10.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) testing on training data (d) correct labels
11. 11.True or false: “never learns from data” describes how a normal program works. (a) False (b) True
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
13. 13.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a strong battery (d) a fair model
14. 14.Which of these is training data? (a) naming a new leaf (b) tomorrow’s rain guess (c) old emails marked spam (d) the learned patterns
15. 15.✨ Is everything a generative AI makes true? (a) no, it can be wrong (b) only on Mondays (c) yes, always
16. 16.🛠️ Which does this job need: suggesting the next word as you type? (a) a simple rule is enough (b) needs AI (learning)
17. 17.🏠 AI or not AI: a digital watch? (a) not AI (b) uses AI
18. 18.True or false: “naming a new fruit photo” is training data. (a) False (b) True
19. 19.Which of these is AI that sorts, spots or predicts (not generative)? (a) turning speech to text (b) writing quiz questions (c) drafting a letter (d) inventing a recipe
20. 20.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) data, model, prediction (d) prediction, data, model
21. 21.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
22. 22.🤖 What does AI stand for? (a) app installer (b) artificial intelligence (c) actual information (d) automatic internet
23. 23.Which of these is generative AI (it makes something new)? (a) unlocking with a face (b) spotting spam (c) writing quiz questions (d) spotting a sick leaf
24. 24.Which of these adds to bias? (a) skip testing (b) ask different people (c) check who is missing (d) add varied examples
25. 25.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
26. 26.True or false: “a summary of chapter 3” asks for an image. (a) True (b) False
27. 27.Which of these is training data? (a) what training builds (b) recorded spoken words (c) tomorrow’s rain guess (d) the trained program
28. 28.🏠 AI or not AI: a kitchen weighing scale? (a) not AI (b) uses AI
29. 29.🔍 Which is the odd one out? (a) an AI chatbot (b) an image generator (c) a light switch (d) face unlock on a phone
30. 30.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) blurry, unclear photos (d) photos in many lights
31. 31.🧐 Fact or myth: “AI learns from data”? (a) a myth (b) a fact
32. 32.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) new data for testing (d) testing on training data
33. 33.🔍 Which is the odd one out? (a) a map predicting traffic (b) an app translating signs (c) a spam filter (d) a calculator
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
35. 35.True or false: counting the words in an essay needs AI that learns from examples. (a) True (b) False
36. 36.📱 What is the AI doing here: a camera app naming the flower you point at? (a) recommending (b) predicting (c) recognising (d) generating
37. 37.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) copies of one photo (d) correct labels
38. 38.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
39. 39.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
40. 40.⏩ In machine learning, which comes first: the trained program or 500 labelled leaf photos? (a) the trained program (b) 500 labelled leaf photos

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