The vocabulary you'll meet in policy documents, staff training, and the classroom – in plain language, precise where it changes decisions.
Agent / agentic AI – AI that takes multi-step actions toward a goal – browsing, running code, operating software – rather than only answering. It changes what “a student used AI” even means.
AI detector – Software claiming to spot machine-written text, mostly via predictability. Unreliable in both directions and biased against non-native and novice writers – indefensible as sole evidence.
AI literacy – The mix of tool fluency, technical understanding, and critical judgement students and staff need – the goal, not just “can they use the tool”.
Cognitive offloading – Using tools to reduce mental effort (lists, GPS, now AI). Often rational – but what you don't practise, you lose.
Context window – The model's working memory: how much text it can attend to at once. Everything in it is transmitted to the provider; nothing outside it exists to the model.
Generative AI – AI that produces new content – text, images, audio, code – rather than sorting existing content. The post-2022 wave this all concerns.
Guardrails – Product-level constraints on behaviour: instructions, filters, refusal rules. In the field's best trial, guardrails were the variable that separated harm from benefit.
Hallucination – Confident, fluent, false output. A structural feature of generation, not a bug awaiting a fix – and worst on specifics: numbers, quotes, citations, recent events.
Human-in-the-loop – Any setup where AI drafts and a human decides. The professional-ownership rule for feedback, grades, and consequential documents.
LLM (large language model) – A neural network trained on vast text to predict the next word; at scale, this produces the fluent, as-if-understanding behaviour behind chatbots.
Process evidence – Drafts, version history, notes and reflections that show how work was made – the fair-process alternative to a detector's verdict, and a pedagogical good in itself.
Productive failure – The finding that struggling with a problem before instruction produces deeper learning – the learning-science case against AI that answers instantly.
Prompt / system prompt – What you type – plus the hidden standing instructions a product inserts before every chat. Most education tools differ mainly at the system-prompt layer.
RAG (retrieval-augmented generation) – Answering from fetched documents – your curriculum, a textbook, the web – instead of the model's memory. Cuts fabrication sharply, but is only as good as what it retrieves.
Sycophancy – The trained tendency to agree with, mirror, and flatter the user. A specific tutoring hazard – it will validate a wrong answer – and a risk for vulnerable users.
Two-lane assessment – Separating secured, supervised assessment of learning (Lane 1) from open, AI-inclusive assessment for learning (Lane 2) – the emerging answer to the validity crisis.