Información de la conferencia

AIED 2026: International Conference on AI in Education

Por favor Iniciar sesión para ver el sitio web de la conferencia
Cuenta gratuita: consulta los sitios oficiales, sigue las fechas límite y recibe recordatorios por correo.
Día de Entrega:
2026-01-26
Fecha de Notificación:
2026-03-23
Fecha de conferencia:
2026-06-27
Ubicación:
Seoul, South Korea
Ediciones:
ICORE: A   QUALIS: B1   Vistas: 40501   Seguidores: 17   Asistentes: 9
Insertar insignia de cuenta atrás
AIED
Obtener estos datos por API
Las búsquedas y las listas de rankings no necesitan credenciales; el detalle completo de esta página requiere una clave de API gratuita. Consulta la guía para desarrolladores.

Índice Conference Partner (CP-I)

71,3 / 100
Puesto n.º 318 de 5.697 congresos · 6% superior

N.º 1 de 140 en Educación y tecnología educativa

Reconocimiento académico (35%)
88
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
90
Atención de la comunidad (10%)
43
Integridad del registro público (15%)
55

Datos utilizados: Categorías: ICORE A, QUALIS B1 · Ediciones documentadas: 27 · Investigadores que lo siguen aquí: 17 · Investigadores que abrieron esta página en los últimos 24 meses: 7

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Premios al mejor artículo (+2,3)
Los organizadores pueden añadirlo desde esta página tras reclamar el congreso; las puntuaciones se recalculan cada noche. Cómo subir esta puntuación

Confianza 80 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-10-12

Solicitud de Artículos

AIED 2026 (International Conference on AI in Education) is a ICORE A / QUALIS B1 conference held in Seoul, South Korea on 2026-06-27. The paper submission deadline is 2026-01-26. Acceptance notifications are sent on 2026-03-23.

Discover the AIED 2026 tracks This year's conference invites both empirical and theoretical contributions to the core AIED research within the following three tracks. Domain-specific submission can be submitted to different tracks depending on their focus. Track 1 — Technical aspects of AIED Focus The computer science and engineering dimension of AIED. The primary contribution must be technical. Representative topics Novel AIED architectures and system engineering Novel cognitive systems and computational reasoning Knowledge representation and domain modeling Agentic AI and (multi) agent systems Machine learning, deep learning, generative models, NLP, and video modeling Data mining, feature engineering, predictive modeling Multimodal systems AIED systems for special education Frugal systems in AIED Intelligent tutoring system implementation Evaluation through computational metrics (accuracy, validity, reliability) Keywords Intelligent tutoring systems, Pedagogical agents, LLMs, Classifiers, Predictive models, Clustering, Data mining, Multi-agent architectures, Proof-of-concept, Knowledge representation Track 2 — Human aspects of AIED Focus Human learning, pedagogy, and theoretical foundations. The primary contribution must come from learning science and humanities. Representative topics Studies on agency, co-regulation, and human-AI collaboration Empirical evaluation of AIED systems (quantitative, qualitative, or mixed methods) Learning sciences perspectives on AIED Pedagogical theories and instructional design Human cognitive, motivational, metacognitive, or affective processes Collaborative, informal, or game-based learning studies Analyses focused on understanding learners, teachers, or educational phenomena Keywords Pedagogy, Cognitive states, Affective states, Learning theory, Learner engagement, Self-regulation, Learning processes, Motivation, Metacognition, Social learning, Lifelong learning, Collaborative learning Track 3 — Societal aspects of AIED Focus Ethics, equity, policy, and societal dimensions of AIED. The primary contribution must address ethics, equity, policy, or societal dimensions of AIED. Representative topics Algorithmic fairness, bias, transparency, accountability Privacy, surveillance, data governance Cultural awareness in AIED Low-resource and development contexts; addressing the AIED divide Socio-economic, gender, and racial equity in AIED Ethical frameworks for AIED deployment Policy implications for AI in education AI literacy and critical AI education when focusing on societal implications Keywords Bias, Fairness, Transparency, Inclusion, Privacy, Governance, Responsible AI, Under-resourced contexts, Societal impact
Última actualización por Admin Agent el

Conferencias Relacionadas

CCFICORECP-IAbreviaciónNombre CompletoEntregaFecha de conferencia
42,7AI+EInternational Conference on AI & Engineering2027-01-252027-03-26
53,1AI'International Conference on Artificial Intelligence and Applications2026-10-112026-12-26
AA*95,6SIGIRInternational Conference on Research and Development in Information Retrieval2027-01-142027-07-18
AA*97,8AAAIAAAI Conference on Artificial Intelligence2026-07-212027-02-16
AA*94,5CVPRIEEE Conference on Computer Vision and Pattern Recognition2026-11-102027-06-20
BA*89,7ICRAInternational Conference on Robotics and Automation2026-09-152027-05-24
BA*94,2IJCAIInternational Joint Conference on Artificial Intelligence2027-01-042027-08-07
AA*92,7STOCACM Symposium on Theory of Computing2026-11-022027-06-06
C87,5ICCInternational Conference on Communications2026-10-022027-05-30
CB62,8IJCNNInternational Joint Conference on Neural Networks2027-01-312027-06-14

Revistas Relacionadas

CCFNombre CompletoFactor de ImpactoEditorISSN
AArtificial Intelligence4.6Elsevier0004-3702
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

Comentarios 0

Aún no hay comentarios.

Por favor Iniciar sesión para publicar un comentario