AI in Education Glossary

The umbrella term: what AI actually does across teaching, administration, and operations.

AI that plans multi-step tasks and takes actions — and the advisory-vs-autonomous line that matters legally.

The architecture that makes AI answer from your documents, with citations, instead of improvising.

What separates an AI-native learning platform from an LMS with a chatbot bolted on.

Practice partner, Socratic guide, or advisory agent — the three modes and how to evaluate vendor claims.

The competency frameworks (ISTE, UNESCO) districts use to teach students and staff to use AI well.

The six components every district AI policy needs, from acceptable use to audit cadence.

The operational uses that save admissions teams hours — and the evaluative uses to avoid.

What AI feedback tools actually do, and why the grade always stays with the instructor.

How AI turns course material into practice questions teachers review and assign.

Advisory attendance flags and the human-review step that keeps them defensible.

AI advisory capabilities across the whole campus stack, from scheduling to resource planning.

AI-drafted lesson structures teachers adapt — a drafting aid, not a curriculum author.

Content scope, spending budgets, audit logs, and human-review gates — the enforcement layer of AI governance.

AI that drafts and proposes while staff decide: how agentic systems work inside OpenEduCat.

Connecting your institution’s own AI model endpoint while keeping platform guardrails.

RAG chat grounded in institutional documents — the governed alternative to consumer chatbots.