会议信息

AIED 2026: International Conference on AI in Education

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截稿日期:
2026-01-26
通知日期:
2026-03-23
会议日期:
2026-06-27
会议地点:
Seoul, South Korea
届数:
ICORE: A   QUALIS: B1   浏览: 40503   关注: 17   参加: 9
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AIED
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会伴指数 (CP-I)

71.3 / 100
全站第 318 名 / 共 5,697 个会议 · 前 6%

教育与教育技术 第 1 / 140

学术认可 (35%)
88
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
90
社区关注 (10%)
43
资料公开度 (15%)
55

用到的输入: 收录等级：ICORE A, QUALIS B1 · 有据可查的届次：27 · 在会伴关注它的研究者：17 人 · 过去 24 个月打开过本页的研究者：7 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-12

征稿

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
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