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AIET 2027: International Conference on Artificial Intelligence in Education Technology

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截稿日期:
2027-02-20 还有 147 天
通知日期:
2027-03-20
会议日期:
2027-07-28
会议地点:
Cologne, Germany
届数:
浏览: 7861   关注: 0   参加: 0

会伴指数 (CP-I)

49.4 / 100
全站第 1,725 名 / 共 5,687 个会议 · 前 31%

教育与教育技术 第 34 / 139 人工智能与机器学习 第 177 / 741

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
59
社区关注 (10%)
19
资料公开度 (15%)
55

用到的输入: 有据可查的届次：8 · 过去 24 个月打开过本页的研究者：11 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 45% —— 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 2026-09-25

征稿

AIET 2027 (International Conference on Artificial Intelligence in Education Technology) is an academic conference held in Cologne, Germany on 2027-07-28. The paper submission deadline is 2027-02-20. Acceptance notifications are sent on 2027-03-20.

We sincerely invite contributions to 2027 8th International Conference on Artificial Intelligence in Education Technology (AIET 2026). AIET 2026 conference will take place in Cologne, Germany during July 28-30, 2027. Topics of interest for submission include as following: Learning Contexts and Informal Learning: Educational games and gamification; Collaborative and group learning; Social networks; Inquiry learning; Social dimensions of learning; Communities of practice; Ubiquitous learning environments; Learning through construction and making; Learning grid; Lifelong, museum, out-of-school, and workplace learning Inequity and inequality in education: socio-economic, gender, and racial issues. Intelligent techniques to support disadvantaged schools and students. Ethics in educational research: sponsorship, scientific validity, participant’s rights and responsibilities, data collection, management and dissemination Design, Use, and Evaluation of human-AI hybrid systems for learning: Research that explores the potential of human-AI interaction in educational contexts; Systems and approaches in which educational stakeholders and AI tools build upon each other’s complementary strengths to achieve educational outcomes and/or improve mutually Online and distance learning: massive open online courses; remote learning in k-12 schools; synchronous and asynchronous learning; mobile learning; active learning in virtual settings Evaluation in the Context of Education Technologies: Studies on human learning, cognition, affect, motivation, and attitudes; Design and formative studies of AI education systems; Evaluation techniques relying on computational analyses Innovative Applications: Domain-specific learning applications (e.g. language, science, engineering, mathematics, medicine, military, industry); Scaling up and large-scale deployment of AI education systems Intelligent and Interactive Technologies in an Educational Context: Natural language processing and speech technologies; Data mining and machine learning; Knowledge representation and reasoning; Semantic web technologies; Multi-agent architectures; Tangible interfaces, wearables and augmented reality Intelligent Techniques to Support Disadvantaged Schools and Students, Inequity and Inequality in Education: Socio-economic, gender, and racial issues. Ethics in educational research: sponsorship, scientific validity, participant's rights and responsibilities, data collection, management and dissemination Learning Contexts and Informal Learning: Educational games and gamification; Collaborative and group learning; Social networks; Inquiry learning; Social dimensions of learning; Communities of practice; Ubiquitous learning environments; Learning through construction and making; Learning grid; Lifelong, museum, out-of-school, and workplace learning Modelling and Representation: Models of learners, including open learner models; facilitators, tasks and problem-solving processes; Models of groups and communities for learning; Modelling motivation, metacognition, and affective aspects of learning; Ontological modelling; Computational thinking and model-building; Representing and analyzing activity flow and discourse during learning Models of Teaching and Learning: Intelligent tutoring and scaffolding; Motivational diagnosis and feedback; Interactive pedagogical agents and learning companions; Agents that promote metacognition, motivation and affect; Adaptive question-answering and dialogue, Educational data mining, Learning analytics and teaching support, Learning with simulations
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相关期刊

CCF全称影响因子出版商ISSN
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
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