How AI writes stories

• Audience years 5-6 (ages 10-12)
• Duration 90 minutes
• Flavour cutouts

Students start by asking a real AI to write a story about their teacher, then spend the rest of the lesson finding out how it did it—by building their own language models Language model A system that predicts what text comes next, using patterns learned from training data. Your hand-built grid, a cutouts spread, a booklet, and a room holding search sheets are all language models. View in glossary out of paper cutouts Cutouts Printed cards, one per word pair in the text, each showing a previous word in a box and the word that followed it. Spread on a table they are the model: to generate, find a card whose boxed word matches your current word and read off its next word. Common pairs appear on more cards, so picking by eye samples in proportion. View in glossary and generating brand-new stories from the patterns in their favourite picture books. No computers (except yours, for the slides), no maths beyond matching colours.

We most recently ran this lesson at Duffy Primary School with three year 5-6 classes.

Lesson plan#

Allow five minutes for arrival and settling before part 1. If you’re running over time, part 4 is designed to be dropped—parts 1-3 and 5 are the core of the lesson.

Part 1 opens with think-pair-share (“when you hear artificial intelligence, what comes to mind?”), then the fun bit: the class supplies three words, and a slide builds a story prompt starring their teacher that you paste into ChatGPT, Claude, or whichever chatbot Chatbot (ChatGPT, Claude, Gemini) A product that wraps an LLM in a conversation. ChatGPT is OpenAI's, Claude is Anthropic's, Gemini is Google's, and there are many others. On this site we often name ChatGPT or Claude as shorthand for any of them: the concepts apply equally to every modern LLM chatbot, whichever one you use. View in glossary you have access to. A dramatic reading later, you have the hook for the whole lesson: that was a language model, and today we’re going to build one.

Part 2 warms up with predict-the-next-word prompts on the whiteboard (humans are pattern-spotters too), introduces four key words (AI, language model, training Training Building a model by counting patterns in text. When you read through a passage and tally which word follows which, you are training a model. The cutouts, booklets and search sheets are models that the generator has already trained for you from a text. View in glossary , training data Training data The text a model is trained on. In our activities this is the passage you tally, or the book a set of cutouts, a booklet or a set of search sheets was generated from. Modern LLMs are trained on trillions of words from books, websites and other sources. View in glossary ), then runs the Training module in its cutouts flavour: students spread out printed token cutouts from a picture book and discover the patterns.

Part 3 runs the Generation module: starting from a single word, students play the colour-matching game to generate new stories—and read them out loud.

Part 4 (optional) generates one story as a whole class: everyone hunts for the current word in their own cutouts, hands go up, and the story grows one word at a time on butchers paper.

Part 5 wraps up with discussion questions—would you get the same story twice? is the model thinking?—and open Q&A.

You will need#

• printed token cutouts (bigram, i.e. -n 2) from two or three picture books—the Materials page has ready-to-print packs for Green Eggs and Ham, Where Is the Green Sheep?, and We’re Going on a Bear Hunt, or you can generate your own
• a clear table per group of 2-4 students to spread the cutouts on
• pen and paper per group for writing down generated stories
• a whiteboard for the predict-the-next-word warm-up
• butchers paper and a marker for the class story (part 4)
• a projector for the slides, and access to ChatGPT/Claude/etc. for the story-writing demo in part 1

Before you deliver#

• print the cutouts and (ideally) pre-cut them into strips—scissor time is the biggest schedule risk with this age group
• have a few predict-the-next-word prompts ready for the whiteboard; ones the class will finish instantly work best (the school’s own values, or “yer a wizard…” minus the famous last word)
• the story slide in part 1 has fill-in boxes for the teacher’s name, the year level and the three class words, and builds a copy-paste-ready prompt—check you can log in to a chatbot on the room’s computer beforehand

Using the slides

Open the slides in your browser — there's nothing to install or download. There's also a presenter guide (PDF) — every slide followed by a page of its speaker notes — if you'd rather prepare away from the browser.

The slides run in your browser, and every slide carries full presenter notes—what to say, what to watch for, and how long each part should take. A few keys worth knowing:

• f toggles fullscreen—do this before you start presenting
• s opens the speaker view in a separate window, with the presenter notes, a timer, and a preview of the next slide (drag it to your laptop screen and put the slides on the projector)
• o shows an overview of all slides, handy for skipping ahead if you're running short on time