Late Breaking Results (LBR: Posters)

The 27th International Conference on Artificial Intelligence in Education (AIED 2026) invites you to submit your work for consideration in the late breaking results (LBR) track, focusing on the transformative role of Artificial Intelligence (AI) in shaping the future of education. This year’s conference theme, “From Tools to Teammates: Human-AI Synergy For Augmented Learning”, highlights research on human and AI agency, collaborative intelligence, and human & AI co-evolving.

The LBR track offers an opportunity for presenting compelling, preliminary results and innovative work in progress. The goal is to give new, but not necessarily mature work a chance to be seen by other researchers and practitioners and to be discussed at the conference. Accepted submissions will be published in the companion proceedings of the conference and will be presented during the conference as posters. We are seeking original research, conceptual papers, case studies, and innovative projects that explore technical, human, and societal aspects of AIED, in line with the general Call for Papers.

All submissions will be reviewed by the program committee to meet rigorous academic standards of publication.

The review process will be double-blinded, meaning that both the authors and reviewers will remain anonymous. To this end, authors should:

• Eliminate all information that could lead to their identification (names, contact information, affiliations, patents, names of approaches, frameworks, projects and/or systems).
• Cite own prior work (if needed) in the third person.
• Eliminate acknowledgments and references to funding sources.

Papers will be reviewed for relevance, novelty, technical soundness, significance and clarity of presentation. It is important to note that the work presented should not have been published previously or be under consideration in other conferences or journals. Any paper caught in double submission will be rejected without review.