Learn AI,
one lesson at a time.

Every week, somewhere in a boardroom or procurement meeting, a leader approves an AI vendor on the strength of a confident sales deck, an impressive demo, and a reassuring set of certifications. Six months later, that same leader discovers the tool does not perform as expected on their actual data, that customer information has been used to train a model they do not control, and that switching would cost more in time and disruption than they would care to admit. The problem here is rarely a bad vendor. It is a due diligence process that has not caught up with the technology it is meant to evaluate. AI procurement is genuinely different from buying conventional software. The quality of an AI system is not fixed at the point of delivery; it can degrade silently over time. The data you feed it may have consequences you did not anticipate. The vendor's product may be a thin wrapper over a third-party foundation model, a large general-purpose AI system trained on broad datasets, that introduces its own risks and limitations. And as of 2026, the regulatory environment places increasing accountability on the organisations that deploy these tools, not just the companies that build them. This lesson gives you a structured checklist: three categories of scrutiny that separate good due diligence from the superficial kind.

Your students are already using AI. The question in front of every teacher is not whether they should, but whether anyone will teach them to do it well. Most young people have picked up these tools the way they pick up everything else, from each other, and have had no instruction in what the tools actually are, what they get wrong, or when relying on them quietly costs the user something. Schools have solved a version of this before: when the web arrived in classrooms, teaching students to evaluate sources became part of the job. AI needs the same treatment, updated for a technology that does not retrieve information but generates it. This does not require teachers to become technical experts. It requires transferring a discipline every teacher already has, the habit of asking for evidence, to a new kind of source. This lesson covers the misconceptions students bring, the difference between AI use that supports learning and AI use that replaces it, and classroom moves that build the questioning habit without needing specialist tools or extra curriculum time.

You give a chatbot a scrappy event notice and receive a polished invitation. It gets the tone right, tidies the sentences and adds a ticket price you never mentioned. How can the same system be so useful and so wrong? The answer starts with how a language model learns and how it uses information when you ask a question. We will follow a fictional community workshop from its first notice to its final invitation, looking inside each step without equations or code. Along the way, you will compare different ways to write an invitation, untangle a changed plan and repair an AI-generated response. Everything you need is included in the lesson.