On this page
Concepts
Every concept page on the site, in the same sections as the left sidebar. Each one says what an idea in AI and education means, what the research found about it, and which studies it draws on. AI in Education is the umbrella that ties them together.
The concept pages also come as an EPUB or PDF for reading offline. To start from a question, a paper, a tool, a search, or your own assistant instead, see the FAQ, the journal, the resources, search, or the AI page.
220 concept pages, grouped into 10 sections.
Foundations of AI in education
The umbrella view of the field itself: what AI in education is, how it evolved, and the cross-cutting themes (AI literacy, human-AI collaboration, agency) that shape every other strand.
Essentials
The field
Cross-cutting themes
Theories and frameworks
Learning and instruction
How AI supports teaching and learning, through the pedagogies, learning theories, and learner-facing processes AI operates within.
Core pedagogies
- Pedagogies and Teaching Strategies
- Pedagogical Patterns
- Active Learning
- Collaborative Learning
- Project-Based Learning
- Problem-Based Learning
- Productive Failure
- Inquiry-Based Learning
- Experiential Learning
- Game-Based Learning
- Learning by Teaching
- Scaffolding
- Socratic Method
- Critical Pedagogy
- Pedagogical Partnerships
- Storytelling in Education
- Online Teaching and Learning
- Video in Education
Learning theories and processes
- Learning Theories
- Behaviorism
- Constructivism
- Cognitive Psychology
- Sociocultural Learning
- Distributed Cognition
- Situated Learning
- Embodied Learning
- Community of Inquiry
- Self-Regulated Learning
- Self-Determination Theory
- Motivation
- Self-Efficacy
- Self-Directed Learning
- Metacognition
- Desirable Difficulties
- Transfer of Learning
- Prior Knowledge
- ICAP Framework
- Misconceptions about AI
- Refutation Text
- Activity Theory
- Retrieval, Spacing and Interleaving
Learner engagement and experience
Lifelong and professional learning
Technologies and techniques
The technical layer: the AI models and methods that power education technology, and the learner-modeling and tutoring systems built on them.
Models and techniques
- Technologies
- Machine Learning
- Generative AI
- Large Language Models (LLMs)
- RAG (Retrieval-Augmented Generation)
- Prompt Engineering
- LLM Training and Fine-Tuning
- Vibe Coding
- Multimodal AI
- Speech and Voice Technologies
- Visualization
- Educational NLP
- Reinforcement Learning
- Knowledge Graph
- Robots in Education
- Conversational AI
- Simulation
- Virtual and Augmented Reality
Learner modeling and adaptive systems
Platforms, openness and adoption
AI in the disciplines
How AI is applied across academic subjects and educational levels: the field’s subject-area coverage.
Subject areas
- AIEd in the Disciplines
- Math Education
- Physics Education
- Chemistry Education
- Biology Education
- CS Education
- Engineering Education
- STEM Education
- Science Education
- Writing
- Language Learning
- English Education (EAP / EFL / ESL)
- Environmental Education
- Business Education
- Humanities and Social Science Education
- Arts, Design and Media Education
- Design Education
- Medical and Health Professions Education
- Nursing Education
- Legal Education
- Information Technology Education
Research fields
Levels and contexts
Levels and settings
Bands and comparisons
Assessment and measurement
How AI both assesses learners and reshapes how we evaluate AI systems and their impact.
Assessment and feedback
Research methods and evaluation
Research methods
Evaluation of AI systems
People
Institutions and policy
Equity, ethics, and responsible use
Fairness, access, and responsibility are central to AI in education.