AI in Education

AI in Education (AIED) — the broad, interdisciplinary field that applies artificial intelligence to teaching and learning, and studies its design, use, evaluation, and consequences. As the knowledge base's umbrella concept, AI in education encompasses AI for education (using AI to improve instruction and assessment) and education about AI (developing AI literacy and critical understanding). It sits at the intersection of instructional technology, learning science, computer science, educational policy, Ethics, and equity. This page is an introduction to the field and a map to every concept the knowledge base covers.

Questions to Consider

• 'AI in education' spans two directions: AI for education (using AI to improve teaching) and education about AI (building literacy and critical understanding). Which is more familiar to you, and which do you tend to overlook?
• The field's history is framed as a recurring tension between control and learner agency. As you watch AI tools being adopted, where do you notice that same tension playing out today?
• AI in education sits at the intersection of technology, learning science, policy, ethics, and equity. Which of those lenses do you naturally apply when evaluating an AI tool — and which are you likely to forget?
• This knowledge base organizes the field into strands: pedagogy, learning theories, technologies, disciplines, assessment, feedback, stakeholders, and governance. If you were mapping your own use of AI, which strand would you find yourself in?
• AIED includes teaching students to use AI critically as a goal in itself. In your context, is AI treated more as a subject to be taught or a tool to be used — and does that balance reflect what learners actually need?
• Students may learn AI literacy by using AI critically, not just by learning about AI. How might hands-on, critical use build understanding that passive instruction cannot?

Introduction

AI in education is the umbrella that all other concept pages collectively define. The knowledge base organizes the field into the major strands below, each linking to the relevant concept pages.

A landmark historical perspective, Mishra et al. trace AIED from cybernetics and the 1956 Dartmouth conference through cognitive tutors and Papert's constructionism, arguing that today's GenAI debates re-enact the field's foundational control-vs-Agency tension.

How the knowledge base is organized: the umbrella pages

The knowledge base's concept coverage is anchored by several umbrella pages that group related concepts into navigable strands. These are good entry points for exploring the field:

• AI in Education — this page, the field's overview and map to all coverage.
• AI literacy — the umbrella for understanding, using, and critically evaluating AI, spanning prompt engineering, critical thinking, AI ethics, and responsible use. Alongside it, human–AI collaboration and agentic AI frame how people and AI work together.
• Pedagogies and teaching strategies — the umbrella for how teaching happens: the teaching methods and strategies AI operates within (active, collaborative, project-based, problem-based, experiential, game-based, Socratic, Scaffolding, and more), including the distinct context of online teaching and learning.
• Learning theories — the umbrella for how learning happens: the theoretical frameworks (Behaviorism, cognitivism, constructivism, sociocultural, cognitive, motivational) that shape AI design and evaluation.
• Technologies — the umbrella for the technical layer: the AI systems (LLMs, generative AI, Multimodal, robotics) and techniques (RAG, prompt engineering, reinforcement learning, model training, agentic orchestration) that power AIED. The learner-modeling family — knowledge tracing, cognitive diagnosis, simulating students, and the systems that consume them (intelligent tutoring, adaptive learning, personalized learning) — is grouped under the Learner Modeling and Adaptive Instruction umbrella within this technical strand.
• AI in the disciplines — the umbrella for how AI is applied across subject areas (mathematics, physics, language learning, computer science, writing, STEM, engineering, business, teacher education, health professions, and more) and educational levels (K-12, higher education, adult learning).
• Assessment (with formative, summative, authentic, and automated strands) — the umbrella for how AI both assesses learners and reshapes assessment validity and integrity.
• Feedback — the umbrella for how feedback is generated, delivered, and used: the feedback loop, feedback quality, feedback literacy, and its assessment contexts (formative, peer, automated).
• Stakeholders in AI education — the umbrella for who the actors are: learners, teachers, learning designers, administrators, and policymakers.
• AI ed evaluation and research methods — the umbrellas for how we know whether AI works: efficacy studies, benchmarks, randomized controlled trials, meta-analysis, and learning gains as the core outcome measure. Readers should also weigh the cross-cutting limitations of this evidence.
• AI governance, educational AI policy, and equity — the umbrellas for the institutional, regulatory, and fairness layer (see also Regulation and Privacy).

These umbrella pages are linked throughout the sections below; each strand below names both its umbrella and its constituent concepts.

Two dimensions of AI in education

AI in education research spans two interconnected directions:

• AI for education — using AI systems to enhance teaching, learning, assessment, and administration. This includes AI tutoring, adaptive learning, personalized learning, automated essay scoring, automated question generation, automated assessment, formative assessment, learning analytics, and feedback loops.
• Education about AI — teaching learners and educators to understand, use, and critically evaluate AI. The core is AI literacy, supported by prompt engineering, critical thinking, AI ethics, governance education, digital literacy, and responsible use.

These two dimensions are not separate: using AI well requires understanding it, and teaching about AI is enriched by using it. This human-AI collaboration is a central theme.

Foundations of AI in education

The field's cross-cutting and foundational concepts anchor the knowledge base's coverage and appear first in the sidebar. They open with an Essentials group — the concepts every reader should start with: the umbrella itself, misconceptions about AI, AI literacy, agentic AI, cognitive offloading, how AI use is framed for students, reducing AI misuse, academic integrity, teaching, learning design, and educational development. The field strand then covers the field's history, the cross-cutting limitations of the evidence base, its philosophy, and theory development. The cross-cutting themes — human–AI collaboration, learner agency, learner identity, design thinking, curriculum design, critical thinking, Sustainability, and computational thinking — cut across every strand, because the inaccurate mental models people hold about AI are upstream of misuse and under-calibrated trust.

Learning and instruction

How AI supports teaching and learning is the heart of the field. Key concepts include:

• Core pedagogies: pedagogies and teaching strategies — the umbrella for the knowledge base's teaching-methods coverage — along with active learning, collaborative learning, group work, project-based learning, problem-based learning, productive failure, inquiry-based learning, experiential learning, game-based learning, learning by teaching, Scaffolding, the Socratic method, critical pedagogy, pedagogical partnerships, storytelling, learning design, online teaching and learning, and video in education.
• Learning theories and processes: the learning theories umbrella (Behaviorism, cognitivism, constructivism, sociocultural, distributed cognition, situated learning, embodied learning, community of inquiry) sits alongside learner-facing processes like self-regulated learning, self-determination theory, Motivation, Self Efficacy, self-directed learning, Metacognition, desirable difficulties, transfer of learning, prior knowledge, ICAP cognitive engagement, refutation text, and activity theory.
• Learner engagement and experience: student engagement, Help Seeking, social-emotional learning, Well Being, Creativity, and student–AI interaction shape how learners actually encounter and are affected by AI.

AI technologies and techniques

The Technologies page is the umbrella for the technical layer:

• Models and techniques: generative AI, large language models, retrieval-augmented generation, multimodal models, educational NLP, reinforcement learning, knowledge graphs, robots in education, conversational AI, Simulation, and training pedagogical LLMs. Newer interaction styles belong here too — most prominently vibe coding, the natural-language-driven workflow in which the user specifies a program by prompting an LLM and judges the resulting behavior rather than reading or editing source, which reframes programming as an act of expression and verification and lowers the barrier to end users building their own tools. Integration-depth frameworks such as SAMR and adoption theories such as TAM classify how deeply AI is taken up and how much it transforms the task.
• Learner modeling and adaptive systems: the technical systems that represent and adapt to the learner are grouped under the Learner Modeling and Adaptive Instruction umbrella — knowledge tracing, cognitive diagnosis, simulating students, intelligent tutoring, adaptive learning, personalized learning, pedagogical agents, affective tutoring, affective computing, human-in-the-loop AI, and learning analytics. These sit at the technical layer because they are the AI systems themselves, distinct from the pedagogies they enact.

AI in the disciplines

AI is applied across disciplines and educational levels. The knowledge base's overview of AIEd in the disciplines maps subject-area coverage:

• Subject areas: mathematics, physics, chemistry, biology, computer science, engineering, STEM, writing, language learning, English education (EAP/EFL/ESL), business, economics, and management, humanities and social sciences, and medical and health professions.
• Levels and contexts: K-12, Early Childhood Education, higher education, adult learning, special education, and teacher education. Domain-adjacent concepts include universal design for learning, Neurodiversity, multilingual learning, and social-emotional learning.

Assessment, evaluation, and measurement

AI transforms both how we assess learners and how we evaluate AI systems themselves:

• Assessment and feedback: Assessment, formative assessment, summative assessment, authentic assessment, e-portfolio, Feedback and feedback literacy, AI feedback quality, peer review, automated assessment, automated essay scoring, and automated question generation. Because a model can now produce plausible finished work on demand, the knowledge base foregrounds the capability that remains the learner's own: evaluative judgement, the capacity to appraise the quality of one's own work, peers' work, and AI output against reasoned criteria. It is the construct several feedback and authentic-assessment studies converge on — the hybrid feedback condition outperforming direct AI feedback in a multisite experiment, the sustainability gap in AI formative feedback, and the practical move of assessing the decisions students make rather than only the artefact — and it is a core reason AI-era redesign shifts from detection toward tasks whose integrity survives inspection. Group work is likewise assessed along both process and product, where teams must negotiate whose and what kind of AI engagement counts as acceptable.
• Measurement and validity: assessment validity, psychometrically aware AI, educational measurement, item response theory, AI detection, remote proctoring, and academic integrity.
• Evaluation of AI systems: AI ed evaluation, benchmarks, efficacy studies and research methods (qualitative, quantitative, mixed-methods, design-based, usability), randomized controlled trials, meta-analysis, learning gains, network analysis, and the cross-cutting limitations of this evidence.

People: learners, teachers, and institutions

AI in education changes the roles of every stakeholder. The knowledge base's Stakeholders in AI education page is the umbrella covering all of them:

• Learners: student experience, career development and readiness, and AI anxiety and stress shape how students encounter AI.
• Instructors: teacher AI competency, technological pedagogical content knowledge (TPACK), educational development, and pedagogical safety address educator preparation and support.
• Institutions: Administratorss, educational AI policy, AI governance, technology adoption, AI regulation, open source, edtech platforms, professional and lifelong learning, and professional training cover the institutional and societal layer.

Equity, ethics, and responsible use

Fairness, access, and responsibility are central to AI in education:

• Equity and access: Equity, digital divide, bias mitigation, culturally relevant pedagogy, multilingual learning, inclusive learning, Accessibility, assistive technology, Neurodiversity, universal design for learning, and Global South studies.
• Ethics and responsibility: AI ethics, AI misuse and learning harm, AI use disclosure, Guardrails, Privacy, hallucination risk, AI sycophancy, Trust, trust calibration, reducing AI misuse, how AI use is framed for students, and cognitive offloading.

A systematic review of the field's ethics literature (Agarwal et al. 2026, 25 articles) consolidates AIED ethics into six main ethical values — non-discrimination, data stewardship, human oversight, goodwill, explicability, and educational aptness — and maps the ethical norms onto a stakeholder-by-value matrix. It finds end users largely passive in the ethical literature (student voices essentially absent) and calls for integrating ethics into AIED design and a greater focus on the educational (pedagogical) dimension of AIED ethics.

Emergent and cross-cutting themes

Several themes cut across the field:

• Trust and critical use: Trust, trust calibration, AI sycophancy, critical thinking, cognitive offloading, critical pedagogy, and reducing AI misuse (see also how AI use is framed for students). How learners and teachers decide to adopt and rely on AI is modeled by technology acceptance research, while Global South studies foreground equity and cultural context in adoption. Empirically, how AI explains itself shapes this trust: Feldman-Maggor et al. (2025) showed that explainable AI builds teachers' trust in AI recommendations through understandability, with domain-driven (curricular-language) explanations trusted and accepted more than data-driven feature-importance output.
• The evolution of the field: the knowledge base traces AI in education from early intelligent tutoring systems and knowledge tracing to LLM-driven tutoring, agents, and agentic AI — a rapid shift from tool-centric studies to sociotechnical frameworks (design thinking, curriculum design, institutional change), and from hand-authored systems to user-driven workflows in which a learner or non-programmer specifies behavior in natural language (vibe coding). Rismanchian & Doroudi formalize this trajectory with their AI×Ed framework, tracing papers across four decades of proceedings to show that the field moved from a diverse mix — including substantial research treating AI as an analogy to human intelligence and learning — toward a near-exclusive focus on applied, data-driven, researcher-facing uses, a turn that the rise of LLMs now appears to partly reverse (three of the four "AI-as-analogy" papers at AIED 2024 were LLM-based).
• Emotion, anxiety, and career futures: AI induces and shapes emotional responses — AI anxiety and stress spanning proctoring surveillance, integrity fears, and career displacement — while career development and readiness addresses how education prepares learners for an AI-disrupted labor market (see also Well Being).

Field maturity

The knowledge base reflects a field in rapid evolution — from early intelligent tutoring systems to LLM-driven tutoring and agentic AI; from detection-focused academic-integrity tools to assessment redesign; from tool-centric studies to sociotechnical and equity-focused frameworks. The evidence base increasingly emphasizes rigorous research methods, evaluation, experimental designs, and long-term outcomes.

Connections

AI in education connects to every concept in the knowledge base — it is the field that all other concept pages collectively define. Use this page as a starting point to navigate the full knowledge base.

Connected Concepts

• Pedagogical Partnerships — Pedagogical Partnerships
• Early Childhood Elementary AI Education — Early childhood and elementary AI education (young children)
• AI Anxiety And Stress — AI anxiety and stress in education
• Career Development And Readiness — Career development and readiness
• AI Literacy — AI literacy
• Misconceptions — Misconceptions about AI
• AI Technologies — AI technologies and techniques
• Intelligent Tutoring — Intelligent tutoring systems
• Cognitive Psychology — Cognitivism / cognitive psychology
• Generative AI — Generative AI
• LLM — Large language models
• AI Ed Evaluation — AI ed evaluation
• Pedagogy — Pedagogies and teaching strategies
• Research Methods AIED — Efficacy research methods
• Limitations In AIED Research — Limitations of the AIED evidence base
• Assessment — Assessment
• Evaluative Judgement — Evaluative judgement
• Group Work — Group work
• Feedback — Feedback
• Learning Analytics — Learning analytics
• Personalized Learning — Personalized learning
• Adaptive Learning — Adaptive learning
• Stakeholders — People and audiences in AI education
• Teacher Role — Teaching
• Teacher AI Competency — Teacher AI competency
• Human AI Collaboration — Human-AI collaboration
• Equity In AI Education — Equity in AI education
• Ethics — AI ethics
• AI Use Disclosure — AI use and disclosure statements
• Governance — AI governance
• Educational Policy AI — Educational AI policy
• Educational Robotics — Robots in education
• Change Management — Change management and institutional reform
• Machine Learning — Machine learning as the technical foundation
• Samr Model — The SAMR model of technology integration
• Vibe Coding — Vibe coding
• Problem Solving — Problem solving with and without AI
• Mastery Learning — Mastery learning and adaptive instruction
• Science Education — Science education across the disciplines
• Visualization — Data visualization, infographics, and dashboards
• Video Education — Video in Education: video as a medium and AI-generated/personalized/analytics of video learning

Connected Articles

• Ssail Safe Sound AI Learning 2026 — SSAIL: A Design Framework for Safe and Sound AI for Learning

• Icet ML Education Trust 2026 — Addressing Trust in AI Systems through Education: A Didactic Perspective

• Student Attention Estimation Fairness 2026 — Fairness-Aware Multimodal Transformer Modeling for Real-Time Student Attention Estimation

• Mindful LLM Math Tutoring 2026 — Beyond Problem Solving: Large Language Models for Emotional and Reflective Support in Mathematics Learning

• Pedagogy First Technology Second Teacher Knowledge 2026 — Teacher professional knowledge and student learning in K-12 AI education (Shen et al. 2026)

• Guided Inquiry GenAI Course Policy 2026 — Students co-designing GenAI course policies via guided inquiry (Hingle & Johri 2026)

• School AI Education Readiness Gaps Agency 2026 — School AI education narrows psychological but not cognitive readiness gaps

• Learning Context Framework Context Aware AI Education 2026

• Banihashem AI SRL Systematic Mapping Review 2025

• Mishra Control Vs Agency History 2025 — Control vs. Agency: a historical overview of AI in education

• Raza Farooq AIED Review 2020 2025 — A comprehensive review of AIED research

• Liang GenAI Systematic Review Human AI 2026 — GenAI in education: systematic review

• Institutional Change Framework AI — Institutional change framework for AI

• Reconceptualizing Community Inquiry Generative AI — Reconceptualizing Community of Inquiry for GenAI

• Fostering Collaborative Futures AI Ecosystems 2026 — Fostering collaborative futures: AI integration in educational ecosystems

• Alrahmi Org Drivers AI Adoption He 2026 — Exploring organisational drivers of AI adoption in higher education

• AI Online Education Engagement Satisfaction 2026 — AI in online education: impact on learner engagement and satisfaction

• AI Decision Support Online Learning Assessment 2026 — AI-driven decision support for online learning and assessment

• Voicu AI Interpretive Cognition Ssh 2026 — AI-mediated learning and the restructuring of interpretive cognition

• White Wu Robotics AI Education 2026 — Robotics and AI in education

• GenAI Policies Higher Ed Computing — GenAI policy in computing

• Metacognitively Discordant Completion GenAI 2026 — Metacognitive discord in GenAI completion

• Academic League Of AI 2026 — Academic League of AI: teaching, research, and extension

• Zhao GenAI Higher Order Thinking Meta 2026 — GenAI and higher-order thinking meta-analysis

• Bassett AI Detectors Education 2026 — Heads we win, tails you lose: AI detectors in education (Bassett et al. 2026)

• Kim AI Andragogy 2026 — AI applications in supporting andragogy (Kim et al. 2026)

• Aivaluate Anxiety Assessment 2026 — AIvaluate: LLM-augmented assessment of student anxiety (2026)

• Educational Robotics Pathways 2026 — Pathways to learning AI-powered educational robotics (2026)

• AI Ethics Bibliometric 2026 — AI ethics and professional judgement: a bibliometric analysis (Mazlan et al. 2026)

• Motivation Shape Future Education AI Switzerland China — Motivation to shape the future of education with AI

• Raffaghelli Situated AI Ethics 2026 — Situated AI ethics: a cultural-historical and ecological framework for education

• Li Mroziak Reorienting Critical AI Literacy — Reorienting critical AI literacy

• Prezenski Human Centered AI Aided Learning — How human-centered is AI-aided learning?

• Ojeda Ramirez Community Based AI Learning — Community-based AI learning

• Avraamidou AI Colonization Science Education — Disrupting the AI colonization of science education

• Bin Bakheet Adaptive AI STEM Deep Learning 2026 — Adaptive AI-based STEM program for deep learning

• Strydom Human Gai Paradigms 2026 — Framing human-AI dynamics: seven GAI engagement paradigms (Strydom 2026)

• Mechanical Compliance Human Flourishing AI Literacy 2026 — Socialist humanist AI literacy + fair use

• Reflective Triangle Model Teacher AI 2026 — Reflective Triangle Model: AI as cognitive mediator

• Generative AI Mediational Agent Sociocultural 2026 — Generative AI as a mediational agent

• Credentials Carry Evidence AI Agents 2026 — Credentials that carry their evidence for AI-agent work

• Caruana Pre University AI Education Slr 2026 — Preparing learners and teachers for an AI-driven future: SLR of pre-university AI education (Caruana et al. 2026)

• Alsuhaymi Sustainable Education AI Digitalization 2026 — Value-critical approach to sustainable education and AI (Alsuhami & Atallah 2026)

• Cogevol Learning Environment Generation 2026 — CogEvol: Learning Environment Generation

• Policy Deficit AI Sel 2026 — The Policy Deficit in AI × SEL Research

• Agarwal Ethical Values Norms AIED 2026 — Ethical values and norms for AI in education

• XAI Teachers Trust Edtech Recommendations 2026

• Rismanchian AI Education Four Decades Aixed 2026