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.