Explainers

9 min readSeptember 3, 2026

A glossary of AI terms for schools

Every term you are likely to meet in a vendor conversation, defined plainly, with a note on why it matters rather than only what it means.

Vendor calls and policy documents are full of terms used loosely and sometimes deliberately. These are the ones that turn up, defined plainly, each with a note on what it actually means for a school.

The basics

Large language model (LLM)

The system underneath a chatbot. It has learned which words tend to follow which, and answers by producing plausible continuations rather than retrieving facts. Why it matters: this is why it invents citations, and why that is a property to manage rather than a bug awaiting a fix.

Prompt

What you type. Also used for the hidden instructions a product adds before your text — the "system prompt" — which is where a product's personality and some of its rules live.

Token

The chunks of text a model reads and writes, roughly three quarters of a word each. Why it matters: usage limits and prices are usually stated in tokens, so a "1 million token" allowance is about 750,000 words.

Context window

How much text the model can consider at once, including the whole conversation so far. Why it matters: in a long conversation, the earliest part eventually falls out, and the model appears to forget what it was told.

Hallucination

The industry's term for the model stating something false with confidence. A poor word — it implies a malfunction, when it is the ordinary mechanism producing a plausible-but-untrue continuation.

Safety and filtering

Content filter

A check applied to requests, answers or both. Why it matters: ask which. A product that screens only the request cannot catch an unsuitable answer to a reasonable question, which is most of the risk with images.

Pre-filter and post-scan

Checking before generation, and checking the output afterwards. A serious product does both.

Fail open / fail closed

What happens when the safety check itself is unavailable. Fail open lets the request through unchecked; fail closed refuses it. Why it matters: this is the single most revealing question to ask a vendor, and a filter that fails open switches itself off exactly when nobody is watching.

Guardrails

A loose marketing term covering anything from a filter to a phrase in the system prompt. Why it matters: it means nothing on its own. Ask what the guardrails actually are and where they run.

Alignment

How well a model's behaviour matches its makers' intentions. Why it matters: note whose intentions. A well-aligned general model is aligned to its vendor's global policy, not to your school's standards.

Jailbreak

A prompt crafted to get around a model's restrictions, usually by role-play or hypothetical framing. Why it matters: students share these. A filter that runs outside the model, on the request and the answer, is not defeated by persuading the model of anything.

Deployment

Server-side vs client-side

Whether something runs on the provider's machines or on the user's device. Why it matters: client-side filtering — an extension, a managed profile — protects a device. Server-side filtering is a property of the account and follows the student to any device.

API

The interface one program uses to call another. Why it matters mainly for a detail: content sent through a provider's API is usually governed by different terms from its consumer chat product, which is why "we do not train on your data" needs to be asked about the specific route your content takes.

Gateway or reseller

A company sitting between a product and the model providers. Why it matters: it is another party receiving your students' prompts, and it is easy to omit from a sub-processor list. Ask explicitly whether one is involved.

Sub-processor

Any third party handling your data on a vendor's behalf. Why it matters: you are entitled to the current list, and it should name the model providers and any gateway.

Data

Training data

What a model learned from. Why it matters: the live question is whether your students' content becomes training data for anyone — the vendor, or the providers behind them. These are different questions and a vendor answering only the first has not answered.

Zero retention

A provider setting where content is not stored after answering. Why it matters: usually available on business tiers and not consumer ones. Ask whether it is enabled on the tier your school is actually on.

Data residency

Which country your data is stored in. Why it matters for schools with legal obligations about transfers; ask before it becomes a question someone else asks you.

Terms used loosely

"AI-powered" — describes nothing. Ask what it does.
"Safe for schools" — a claim, not a specification. Ask what is checked, by whom, and what happens when the check fails.
"Enterprise-grade security" — no fixed meaning. Ask for the specific control you care about.
"Kid-safe" — same. Ask whether you can add your own blocked topics and whether they reach the image tool.
"Custom AI" — usually a system prompt, not a trained model. Worth clarifying, because the two differ enormously in what they can guarantee.

We would rather answer these plainly than have you guess.

Common questions

What does fail closed mean in AI filtering?

That when the safety check itself cannot run, the request is refused rather than allowed through unchecked. The alternative, failing open, means filtering silently stops during an outage.

What is a hallucination in AI?

The industry term for a model stating something false with confidence. The word implies a malfunction, but it is the ordinary mechanism producing a plausible continuation that happens not to be true.

What is an AI gateway or reseller?

A company sitting between a product and the model providers. It is another party receiving your students' prompts and is easily omitted from a sub-processor list, so ask explicitly whether one is involved.

Published by Navōn. How these guides are written and checked.