학회 정보

EAAI 2027: Symposium on Educational Advances in Artificial Intelligence

학회 웹사이트를 보려면 로그인해 주세요
무료 가입으로 공식 사이트 조회, 마감 추적, 이메일 리마인더를 이용할 수 있습니다.
회원가입 로그인
마감 카운트다운 배지 삽입
EAAI
이 데이터를 API로 가져오기
검색과 순위 목록은 자격 증명이 전혀 필요 없습니다. 이 페이지의 상세 데이터에는 무료 API 키가 필요합니다. 자세한 내용은 개발자 안내 페이지를 참고하세요.
투고 마감일:
2026-09-01 오늘 마감
통보일:
2026-11-17
개최일:
2027-02-21
개최지:
Montreal, Quebec, Canada
개최 횟수:
17
조회: 1011   팔로우: 0   참가: 0

회반 지수 (CP-I)

49.5 / 100
전체 5,645개 중 1,562위 · 상위 28%

인공지능·기계학습 분야 734개 중 154위

학술적 인정 (35%) 데이터 없음 — 중립 기준값 50점으로 계산 —
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%)
78
커뮤니티 관심도 (10%)
12
공개 자료 충실도 (15%)
35

사용한 입력: 확인되는 개최 횟수: 17 · 지난 24개월 동안 이 페이지를 연 연구자: 4명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 45% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-09-01

논문 모집

EAAI 2027 (Symposium on Educational Advances in Artificial Intelligence) is an academic conference held in Montreal, Quebec, Canada on 2027-02-21. The paper submission deadline is 2026-09-01. Acceptance notifications are sent on 2026-11-17.

About EAAI EAAI-27 invites AI education researchers and educators to share and discuss advances in teaching and learning about Artificial Intelligence, including the education and training of AI practitioners and users, defined broadly. The EAAI symposium provides a venue for teachers, educators, and researchers to share their innovative approaches to engaging students at all levels in learning about AI-related topics (e.g., search, logic, machine learning, natural language processing, computer vision, robotics, generative AI, AI ethics, AI literacy, AI safety, and others). Please note that submissions focused on the development, investigation, or use of AI to support teaching and learning of non-AI related topics (e.g., science, math, programming, reading, etc) are not within the scope of EAAI-27. EAAI-27 will focus on education about AI, rather than the use of AI to support education in other domains. We encourage authors of such work to consider submitting to the AAAI main track, IAAI-27 (co-located with AAAI-27), or (if running this year) the AI in Education workshop https://ai4ed.cc/. Submission Types EAAI-27 features the following tracks: Main Track, AI Education in K-12, and Model AI Assignments. All submissions are subject to double-blind review, and all accepted submissions will be presented at the symposium. Per AAAI policy, in-person presentations at the symposium are required. Main Track Chairs: Lisa Zhang (University of Toronto Mississauga), Bradford Mott (North Carolina State University), Gosia Migut (Delft University of Technology) The main track invites papers that contribute to advancing AI education and pedagogy: that is, the teaching and learning of Artificial Intelligence topics. We welcome contributions that advance AI education for a range of learners, including but not limited to future practitioners, undergraduates, graduate students, and the general public. Authors with submissions focused on K-12 educators or learners are encouraged to submit to the K-12 track. Submissions may be framed as research papers or as experience reports. Potential topics include: The design, implementation, and evaluation of AI curricula, courses, modules, or learning activities The design, development, and use of tools or resources for enhancing the teaching of and learning about AI The study of pedagogical, instructional, or mentoring techniques that support learning about AI The teaching and learning of generative AI concepts, tools, and technologies Each paper must be submitted to only one of the contribution areas outlined below. Papers will be reviewed for the area to which they are submitted and will not be moved between areas. Area 1: Education Research We invite submissions on research that advances teaching and learning of Artificial Intelligence topics. The hallmark of papers in this area is the presence of clearly articulated research questions or hypotheses, along with a rigorous evaluation to answer the questions. Submissions should draw on learning sciences or pedagogical theory to demonstrate how the work contributes to enhancing educational experiences in AI. Papers in this area will be evaluated based on the clarity of the research questions posed, the soundness of the approaches used to address these questions, and the overall significance of the contribution. Both qualitative and quantitative research are welcome. Area 2: Experience Report and Innovative Practice We invite submissions that share practical experiences, observations, insights, outcomes, and lessons learned in AI education. Papers in this area provide an opportunity for educators and researchers to describe the design, development, and use of AI education modules, approaches, or tools for teaching and learning about AI. Submissions should describe the context of use, the data collected, and, importantly, provide a rich reflection on what did or did not work and why. Contributions should be grounded in relevant literature, clearly articulate their novelty, and offer insights about the experience or tools presented that are valuable to the broader AI education community. AI Education in K-12 Track Chairs: Kate Moore (MIT), Amy Eguchi (UC San Diego), Henriikka Vartiainen (University of Eastern Finland) This track invites submissions focused on the teaching and learning of Artificial Intelligence topics specifically designed for K-12 learners and K-12 teachers. While this track shares an interest in both research and practice with Areas 1 and 2, it is distinctly oriented toward the unique challenges, contexts, and opportunities of K-12 education, including age-appropriate curriculum design, implementation in formal or informal educational spaces, and the development of resources that support K-12 AI literacy and teacher education focused on K-12 AI literacy. In addition to research papers and experience reports, this track solicits submissions on the design, development, and use of resources, curricula, tools, and interventions for K-12 AI education. Submissions should draw on learning sciences or pedagogical theory to substantiate claims about how the work advances educational experiences in AI or about AI — whether by shaping how concepts are introduced, scaffolding student understanding, or broadening access and participation. Quantitative and qualitative results are welcome where available, but are not required; submissions will be evaluated on the strength of their theoretical and/or pedagogical grounding, alignment of design (and implementation when possible) with learning objectives, and potential value to the K-12 AI education community. Submissions should follow the standard EAAI format for an academic paper and include, as appropriate: a description of the resource, curriculum, or intervention; the target age group and educational context; the required setup and resources; the AI concepts addressed; the expected learning outcomes; and, if available, implementation results. Special Track: Model AI Assignments Chair: Todd Neller (Gettysburg College) This special track invites assignments for AI classes for a range of learners. Good assignments take a lot of work to design. If an assignment you have developed may be useful to the broader AI education community, this track provides an opportunity to share it. Model AI Assignments are shared via a public online archive. Accepted Model AI Assignments must be presented in person at EAAI. This track has special submission instructions (http://modelai.gettysburg.edu). Review Criteria Submissions will be reviewed for: Relevance to the track Significance to the intended audience Engagement with prior work Novelty of contributions Technical soundness Clarity of presentation Evaluation of claims/results (as applicable) Engagement with questions of ethics/inclusivity (as applicable) For empirical studies, we suggest that authors consider making use of one of the reporting standards in the SIGSOFT Empirical Standards document (https://www2.sigsoft.org/EmpiricalStandards/docs/standards). This is not mandatory, and submissions not making use of the reporting standards will not be penalized. Notwithstanding, we always aim for high standards in empirical research. Specifically making use of the SIGSOFT Empirical Standards criteria allows us to hold all authors to the same high standards. Every paper is considered on its own merits, and we recognize that deviation from the guidelines may sometimes be desirable.
최종 수정: Dou Sun ()

관련 학회

CCFICORECP-I약칭정식 명칭투고 마감개최일
40.7Artificial IntelligenceInternational Conference on Automation and Artificial Intelligence2022-06-012020-05-21
AA*92.4SIGIRInternational Conference on Research and Development in Information Retrieval2026-01-152026-07-20
AA*97.7AAAIAAAI Conference on Artificial Intelligence2026-07-212027-02-16
AA*90.7CVPRIEEE Conference on Computer Vision and Pattern Recognition2025-11-062026-06-03
BA*89.5ICRAInternational Conference on Robotics and Automation2026-09-152027-05-24
BA*94.0IJCAIInternational Joint Conference on Artificial Intelligence2026-01-312026-08-15
AA*89.2STOCACM Symposium on Theory of Computing2025-11-042026-06-22
C87.1ICCInternational Conference on Communications2026-10-022027-05-30
CB62.5IJCNNInternational Joint Conference on Neural Networks2027-01-312027-06-14
B91.0ICASSPInternational Conference on Acoustics, Speech and Signal Processing2026-09-162027-05-16

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
Advances in Engineering Software5.7Elsevier0965-9978
Journal on Advances in Signal Processing1.9Springer3091-4507
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

댓글 0

아직 댓글이 없습니다.

댓글을 작성하려면 로그인해 주세요