Self-check in your browser
Every checkpoint in the module, with diagnostic feedback. Nothing to install, nothing uploaded, answers stay in your tab.
DEVELOPMENT REVIEW DEPLOYMENT - NOT READY FOR RELEASE
Before you start
The module is built around one example: the Bernoulli-number computation Ada Lovelace set out in 1843, and what happens when you ask a language model to reconstruct it. The arc is deterministic algorithm, then statistical generation, then human verification.
Two things make this work rather than just read. First, every unit has a checkpoint you can pass or fail — a wrong answer tells you which misconception you have hit. Second, there is a verification lab that judges generated code against an exact reference. Both come in two equal routes: one in your browser, one in a terminal. Neither is the lesser option.
You do not have to take all of it, and you do not have to take it the same way as everybody else. Learning paths sets out the routes through the module — how long each takes, and, more usefully, what you will and will not be able to do at the end of each. Read it before you decide how much to take on.
Your work stays with you
The optional progress tools save only in your current browser profile. Browser data can be lost, so keep important answers separately and download regular backups.
How to check your work
Self-check in a terminal
Clone the repository and run the verification lab and the same checkpoints locally, using the Python standard library. Nothing to install beyond Python 3.
The units
0. Human introduction
How to use the module, what AI helped produce, and the learning contract you are agreeing to.
1. Ada's ordered procedure
Lovelace, the Analytical Engine, Note G, and what it takes to specify a computation exactly.
2. What a language model does differently
Next-token prediction set against deterministic execution, and why plausibility is not correctness.
3. Meet the Bernoulli numbers
Enough mathematics to know what correct output looks like — including the two convention traps that make correct programs disagree.
4. Ask AI to generate the routine
A deliberately underspecified prompt, and the plausible mistakes it produces.
5. Verification lab
Run generated code against an exact oracle, find the first divergence, and name the failure mode.
6. Re-prompting and specification
Turning a vague request into testable requirements. Every clause is a bug that already happened.
7. The historical bug
Optional. An error in a respected published table, and the difference between an established fact and a repeated claim. Made optional for length, not for quality — the core path carries its lesson in the misconceptions list.
8. Disclosure and defence
State what AI did, what you checked, and why your answer is right — three different statements.
9. Alles Lüge! First- and second-order reasoning
The closing unit. Where the observer stands when it checks something, why that question is older than AI, and how to sort claims by the evidence actually behind them.
Reference material
- PDF Handbook — the complete course material in a single printable document.
- Glossary — terms used across the module.
- References and source policy — what is cited, and the rules for citing it.
- Historical notes — the authoritative reference for every historical claim, with each marked established or contested.
- Evidence pack template — what you should have produced by the end.
- Course map — how this module fits the wider AI Literacy course.
- Second-order reasoning — the general argument the module is a worked instance of. Background for Unit 9.
- Misconceptions — expected errors across the module, and where each is addressed.
- Authorship and credits — who made what, including which parts were AI-generated.