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AI in Education Knowledge Base

Concept map of AI in EducationA radial map with AI in Education at the center, connected to twelve top-level concepts: Modeling, Learning, Equity, Feedback, AI Literacy, Assessment, Disciplines, Pedagogy, Ethics, Technologies, Evaluation, and Research.AI in EducationModelingLearningEquityFeedbackAI LiteracyAssessmentDisciplinesPedagogyEthicsTechnologiesEvaluationResearch

Welcome to the AI in Education Knowledge Base, a free, public-domain resource on artificial intelligence in education for the people who teach, study, design, or administer it. It summarizes and synthesizes recent open-access educational research. An AI agent writes and updates the pages with editorial guidance from the site developer.

Browse the index in the left sidebar, which is arranged by strand, open the concept index, or search for a topic. Article pages condense one paper into its purpose, methods, and findings; concept pages collect what the research says about one idea and link to the studies behind it. To start from a question, the FAQ covers what visitors ask first, such as course AI policy or whether to run AI detectors. To start from a tool, the resources page lists free tools and collections, with who made each one and what it costs. The map above groups the field into twelve strands, with AI in Education as the central node.

Chat with the knowledge base

The whole knowledge base is also published as machine-readable files, so you can point an AI chatbot or agent at it and get answers that cite the pages. The prompts are here. New pages go into the journal, which has an RSS feed. A weekly newsletter, the AIEd Research Weekly Update, says what those additions mean for instructors, developers, researchers, and administrators. To read offline, download the EPUB or the PDF.

Recommended starting points by audience

Start with the group that matches your role.

Essential concepts

AI literacy, misconceptions about AI, agentic AI, cognitive offloading, and how to interpret and apply this research, including why a single study rarely settles a question. The full list covers every strand.

Instructors

The changing teaching role, learning design, assessment, feedback, academic integrity, framing AI use and reducing misuse. For subject-specific tools and examples, see AI in your own discipline.

Developers

Building for education, the technologies underneath, intelligent tutoring, multimodal AI, learner modeling, and training and adapting a model for teaching.

Administrators

The administrator role, AI governance, institutional policy, legal issues and risks, and change management.

Researchers

Research methods in AIED, theories and frameworks, evaluating an AI in education intervention, educational measurement, measuring learning gains, and the cross-cutting limits of this evidence base.

What is in the knowledge base

Every page is dated, and the counts below reflect what is published right now; the "last updated" date at the end of the list is the most recent edit.