Blue Sky

The AIED Society organizes the AIED conference and is aimed at advancing science and engineering of intelligent human-technology ecosystems that support learning. The AIED 2026 conference will be the 27th of a longstanding series of international conferences, known for high quality and innovative research on AI-assisted systems and cognitive science approaches for educational computing applications. AIED is ranked A in CORE (top 14.9% of all 785 ranked venues), the well-known ranking of computer science conferences.

Given the success of this track during the last three years, AIED 2026 will include the BlueSky special track, which invites papers from researchers, practitioners, and industry partners to reflect upon the progress of AIED so far and envision what is to come. As AI continues to become deeply embedded in everyday educational practice and rapid advances and adoption of Generative AI are reshaping how people learn, teach, assess, and even think about knowledge itself, we must ask: How will AI innovations transform the way we learn? How will human–AI relationships in learning environments evolve over time? AIED will continue to develop beyond augmenting conventional teaching methods and embrace a new era of learning that is dynamic, adaptive, and truly personalized and transformative. Whether it's customizing content to suit individual needs or creating innovative immersive (or AI-augmented) learning environments, AI's potential in education is boundless.

So, what's next for AI in education? This year's BlueSky track embraces the conference theme "From Tools to Teammates: Human-AI Synergy for Augmented Learning." We seek bold visions that reimagine AI not as instruments that merely deliver instruction, but as partners, collaborators, and amplifiers that embed the principles of human agency and thought. Whether we envision new forms of collaborative problem solving, reimagine assessment as a shared human-AI endeavor, or propose new frameworks for AI teammates, we invite contributions that chart ambitious, yet human-centered paths forward.

These are questions meant to begin and not limit the discussion in the BlueSky papers.

The AIED Society values diversity, equity, and inclusion (and related principles under this broad umbrella) as essential and fundamental values for the AIED community to uphold. Thus, in AIED 2026, we incentivize authors to carefully consider diversity, equity, and inclusion when reporting on your work.

Authors should demonstrate awareness of how ethical issues (including but not limited to equity, inclusion, accessibility) impact the content of their paper, also including if available data, methods, tools, approaches, products, and findings. If tools such as ChatBots are used to help in writing papers, this should be acknowledged in the paper.

Authors should attend carefully to inclusive language and framing. This includes thoughtful use of identity-first or person-first language, gender-neutral terminology, and appropriate demographic categories, while avoiding the conflation of distinct constructs such as race and ethnicity or sex and gender. Authors are also encouraged to reflect on how issues of diversity, equity, and inclusion relate to their theoretical frameworks, findings and future visions of AIED. This reflection may include examining how these issues shape assumptions, hypotheses, methods, and interpretations, as well as considering the broader implications of the work for equity and inclusion in learning technologies and AI-infused learning environments.

In reporting samples and analyses, authors should clearly describe the composition of any human-sourced data, including relevant demographic characteristics, whether drawn from participants, corpus data or training datasets. While skewed or non-representative samples do not automatically warrant rejection, authors should acknowledge demographic imbalances, discuss their potential impact on results and conclusions, and, where possible, describe barriers to representative sampling and steps taken to address them. Authors should demonstrate awareness of how equity, inclusion, accessibility, and bias affect their data, methods, and outcomes, particularly in contexts such as educational technology or corpus analysis. Finally, authors are encouraged to justify how demographic variables are handled analytically, including whether and why they are included, excluded, or controlled for, the assumptions underlying their use, and the extent to which effects are independent, interdependent, or intersectional, while clearly articulating both valid and potentially misleading conclusions.

Accepted papers in the BlueSky track will be published in the conference proceedings.

Each accepted paper within the BlueSky track must be accompanied by a unique author registration (i.e., one registration per paper), completed by the early registration date cut-off. Please note that presenters of papers accepted to the BlueSky track are expected to be on-site to give their presentations and interact with the audience, to have the paper included in the proceedings. An online streaming option will be set-up for remote observers.