Workshop Topics

We invite contributions via workshop abstracts or, for Festival of Learning presenters, fairness-focused talks tied to that work (see Submission). Content should relate to fairness in algorithmic decision-making for education. Cross-cutting themes emphasized in the program include: defining fairness in educational contexts; identifying at-risk or marginalized groups; measurement, metrics, and evaluation; bias mitigation; transparency and reporting of fairness analyses; and auditing AIED systems—with attention to both outcome fairness and procedural fairness.

The following areas illustrate the scope (not an exhaustive list):

• Fairness methods design and incorporation
  • Incorporating fairness principles across an AIED system
  • Ensuring fairness across the AIED system lifecycle
  • Integrating fairness with other AIED areas to provide complementary benefits
• Fairness auditing and evaluation
  • Algorithmic auditing procedures in the context of AIED
  • Improving fairness metrics, definitions, and evaluation approaches in AIED
  • Strengthening mechanisms for long-term adherence to fairness in AIED
• Fairness gaps and challenges identification
  • Identifying gaps and challenges in AIED regarding fairness
  • Exploring what we can do better concerning fairness as a research community
  • Challenges to improving fairness in practice
  • Methods for addressing these challenges in adopting fairness
• Procedural Fairness
  • Operationalizing and studying procedural fairness across an AIED system
  • Gaps in current AIED research related to procedural fairness
  • Ensuring procedural fairness in AIED by identifying and addressing practical implementation challenges
  • Topics related to procedural fairness in education

Submission Details

Participant talks come from two sources:

• Workshop abstracts. Authors may submit a 250-word abstract of their planned talk, written in English (references do not count toward the limit). Abstracts should be submitted using the submission form.
• Festival of Learning presenters. Researchers who are presenting work at the Festival of Learning may request a short slot to speak specifically about fairness or ethical aspects of that work—whether fairness is the main contribution or an important angle that was not the paper’s primary focus. Contact the organizers (see Contacts) to coordinate; capacity is limited and subject to the program schedule.

For workshop abstracts:

• All abstract submissions will undergo a single-blind review process conducted by the organizers.
• Submissions will be evaluated based on relevance, originality, significance, and clarity.
• Accepted abstracts will be made available on the workshop website.
• At least one author per accepted abstract must register for and attend the workshop to present.
• Accepted abstracts are presented as short participant talks (5–7 minutes); see Program for Q&A format.
• Presenters are responsible for preparing slides for their talk.

We expect authors, the program committee, and the organizing committee to adhere to the ACM’s Conflict of Interest Policy and the ACM’s Code of Ethics and Professional Conduct.

Program

The workshop is organized into sessions across a half-day. Exact times will be posted when available. The outline is as follows.

1. Keynote

We invite a keynote speaker to present on state-of-the-art research on handling challenges of fairness and harm in machine learning and AI. We prioritize a speaker from outside the AI and education community to offer a fresh perspective, drawing on how other fields navigate ethical and fairness challenges. The keynote is followed by Q&A.

2. Participant talks

After the keynote, we hold short, fairness-focused talks from participants. Talks come from two sources:

• Presenters who submitted an abstract of their talk to the organizers before the workshop; or
• Presenters who are presenting work at the Festival of Learning and wish to give a talk on fairness or ethical aspects of that research—whether those aspects are central to the publication or an important aside that was not the main contribution.

Each presenter has 5–7 minutes for slides tied to their abstract or research. Depending on how many talks we receive, there will be a short Q&A after each talk or after groups of talks.

3. Moderated panel

Finally, we hold a moderated panel with attendees, centered on themes that emerge from the participant talks. Exact themes depend on submissions; possible examples include technical bias, downstream harms, unfairness mitigation, and/or fairness in generative AI.

Register

Please register for the workshop by following the instructions on the AIED 2026 main conference website (registration details for the Festival of Learning 2026 will be linked from there).

Contacts

For general enquiries on the workshop, please send an email to fstinar2@illinois.edu.