LLMs Unplugged
Teaching Resources for a ChatGPT World
Dr. Ben Swift
ACE 2026, 11 February 2026
Acknowledgement of Country
a(nother) talk about AI*/LLMs
they’re A Thing now
Hi, I’m Ben
The problem
everyone uses ChatGPT (classrooms, office work, group chats)
most folks don’t have a good mental model of how it works…
…and this is a problem
The gap
some feel this “don’t know how it works” anxiety more than others, e.g. classroom teachers
CS Unplugged has been teaching computing concepts without computers for 20+ years
- algorithms & data structures
- traditional ML/AI
- LLMs?
What would “LLMs Unplugged” look like?
- end-to-end generation (training → generation)
- modular and low-friction
- broad accessibility (no coding, minimal maths)
(spoiler) it’s live at llmsunplugged.org
Historical roots
Markov (1913) and Shannon (1948)
did this work by hand
counting letter sequences in Pushkin’s Eugene Onegin (Markov)
generating synthetic text through weighted sampling (Shannon)
The core insight
language models work by
- counting patterns in existing text
- exploiting these patterns to generate new text
modern LLMs: same approach, vastly greater scale
The core mechanic
Training: counting word pairs
Text: “See Spot run. See Spot jump.”
Preprocessed: see spot run . see spot jump .
see | spot | run | . | jump | |
|---|---|---|---|---|---|
see | II | ||||
spot | I | I | |||
run | I | ||||
. | I | ||||
jump | I |
the grid is the model
Demo time
Why this works
- the randomness explains why LLMs give different responses to the same prompt
- the weighting explains why output is usually coherent
- this is how LLMs work, just smaller
Lesson structure
Fundamentals
two core lessons (do in order):
- Training—counting patterns into a grid
- Generation—weighted sampling with dice
available in grid and cutouts variants
about 30mins each (but flexible)
Extensions
Pre-trained Generation
More Context
Sampling
Tool Use
In-context Memory
Induction Heads
Word Embeddings
LoRA
RLHF
Synthetic Data
Reception
Notes from the field
over 500 participants over the past year
- senior executives and public servants
- undergraduates (all disciplines)
- high school teachers
- school-age students
LLMs are “just” probability and randomness at scale—not reasoning, not understanding, but sophisticated pattern matching
The shareback moment
the room comes alive during the generation lesson when groups share the output from their model
“fish fish fish red one fish two fish”
“I do not like green eggs and you may see me be I do not…”
Resources
What’s available
- lessons with instructor notes
- interactive widgets
- videos (coming soon)
- software tools for custom n-gram booklets
all CC BY-NC-SA licensed
Next steps
- continued development (esp. extensions)
- teacher training
- evaluation (inc. via partnerships)
- strengthen curriculum links (e.g. ACARA)
- translations
- keen to hear your experiences
This Friday
Lifting the Veil—a hands-on AI workshop for educators with Brimbank Tech School at VU Sunshine Campus
Friday 13 February, 10am–3pm