Patterns and predictions
Patterns and predictions: the model repeatedly guesses the next most likely piece of text, one small chunk at a time, until the answer is complete.
AI for Kids · Class 7 · Module 1 of 9
How Generative AI Works
It is not magic and it is not thinking. It is prediction on an almost unimaginable scale.
8 sessionsSessions 1–8
4 weeksTypical pace
FoundationLevel
Class 7Artificial Intelligence
The big idea
Generative AI predicts what comes next, learned from patterns across enormous quantities of text. Once students understand this one mechanism, both the impressive results and the confident mistakes stop being mysterious.
Why it matters
Understanding how something works is the difference between using it well and being fooled by it. This module makes everything afterwards make sense.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Training data idea
Training data is the vast collection of text a model learned from. What is absent, outdated or skewed in that data shows up in the output.
Why AI can be wrong
AI can be wrong because plausible and true are different targets. It is optimised for one of them.
Did you know?
Language models do not store facts like a database. They store statistical patterns — which is exactly why they can produce a fluent, confident, entirely invented answer.
Common mix-up
“AI searches the internet for the answer.” Usually it does not. Unless a tool explicitly browses, it is generating from learned patterns.
Session by session
Your 8-session journey
Sessions 1–8 of the 72-session year, at two one-hour sessions per week.
- 1
Warm-up and big picture
Where this module fits, what you will build, and a hands-on starter that gets everyone curious about patterns and predictions.
- 2
Patterns and predictions
Guided teaching on patterns and predictions, worked through together with the teacher.
- 3
Patterns and predictions — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Training data idea
Guided teaching on training data idea, building directly on the previous two sessions.
- 5
Training data idea — practice lab
Applied tasks that combine patterns and predictions and training data idea in one piece of work.
- 6
Why AI can be wrong
Why AI can be wrong introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Interactive AI myth-vs-fact board.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Interactive AI myth-vs-fact board
Every module ends with something the student built themselves and can demonstrate. This is the piece that goes into their portfolio and gets explained out loud at the end of session 8.
Try this at home
Ask AI about something extremely local — your street, your school’s history. Watch how quickly plausible invention appears.
It is finished when
- It works from start to finish without breaking
- You can explain every part of it in your own words
- You tested it and improved at least one thing afterwards
- Someone else used or understood it without your help
By the end of this module
Students finishing Module 1 can:
- Explain and use patterns and predictions without prompting
- Explain and use training data idea without prompting
- Explain and use why ai can be wrong without prompting
- Build and finish interactive ai myth-vs-fact board
- Test your own work and correct what you find
- Talk an adult through what you made and why
Word bank
The vocabulary introduced here, in plain language:
- generative AI
- AI that creates new text, images or audio.
- training data
- The material a model learned patterns from.
- prediction
- Choosing the most likely next piece of output.
Module challenge
Think you have got this?
0 XPLevel 1 · Curious Beginner
6 quick questions drawn from this module — vocabulary, the project you build, and a myth-or-fact round. Every wrong answer explains itself, so a mistake still teaches you something.
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