International Workshop on
Fairness in Algorithmic Decision-Making for Education

to be held as part of the 27th International Conference on Artificial Intelligence in Education (AIED 2026) and the Festival of Learning 2026

Workshops: 27–28 June 2026 · Coex, Seoul, Republic of Korea (with support for remote attendance where offered)

The Workshop will be held in room COEX 328.

Workshop Aims and Scope

Education is being reshaped by AI: intelligent tutoring, risk detection, personalization, and unstructured learning with large language models are now commonplace. Ensuring fairness in educational AI remains difficult. Systems depend on algorithmic decisions and large data ecosystems that often under-represent diverse students; biases in data can be amplified into harm and discrimination; and rapid adoption of generative AI has added fairness and ethics questions that the field is still working through.

Researchers already pursue auditing, mitigation, fairness-aware modeling, bias detection in datasets, and fairness metrics for learning settings—yet this work is often fragmented across subcommunities and only loosely connected to broader fairness ideas from the machine learning community. This workshop aims to bridge those gaps through interdisciplinary dialogue and exchange among researchers, practitioners, and policymakers. Through presentations and a panel, we share practical experience, surface open questions, and connect fairness ideas to actionable strategies for real educational systems—especially as generative AI evolves—advancing socio-technical approaches that go beyond purely technical fixes.

We bring together researchers, practitioners, policymakers, and industry partners whose expertise is needed to put fairness and ethics at the center of AI-powered education.

Fair4AIED is a half-day workshop: a keynote (we prioritize a speaker from outside the AI-in-education community), fairness-focused participant talks, and a moderated panel. Details appear under Program.

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

Important Dates

  • Abstract submissions: May 15th, 2026
  • Notifications: May 27th, 2026
  • Workshop day: June 27th, 2026 (Coex, Seoul, Republic of Korea; see the AIED 2026 important dates for co-located workshops)
  • Main conference (AIED 2026): 27 June – 3 July 2026

Workshop-specific deadlines will be posted here when available. AIED 2026 deadlines use 23:59 AoE (Anywhere on Earth) unless stated otherwise on the official important dates page. Upon acceptance, authors will typically have approximately three weeks to prepare slides for their participant talk.

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.

Keynote Speaker

Keynote Speaker

We invite a keynote speaker to present on state-of-the-art research on challenges of fairness and harm in machine learning and artificial intelligence, followed by Q&A. We will prioritize a speaker from outside the AI-in-education community so participants hear how other fields approach ethical and fairness challenges—offering a novel viewpoint for the AIED audience (see Program).

Speaker: Min Kyung Lee

Title: Participatory AI: Designing and Governing AI with Stakeholders

Abstract: AI systems are increasingly deployed with promises of improved productivity and decision-making, yet are often implemented onto people rather than designed with them. Emerging evidence shows that this approach can lead to unrealized benefits, harmful biases, and distrust. In my research, I propose Participatory AI—a set of methods and tools that bring affected stakeholders directly into the design and governance of AI systems to better align AI with human values, priorities, and lived practices. I develop participatory methods that combine (1) co-design approaches, enabling designers, developers, and affected stakeholders to collaboratively reimagine what AI should do, and (2) computational tools that translate stakeholders’ values and priorities into legible, actionable metrics and visualizations. These tools allow non-specialists to shape system behavior—such as fairness and well-being objectives—within their specific organizational contexts. In this talk, I will present applications of these methods across domains, including gig work, knowledge work, and public assistance. Our findings reveal that participatory methods not only uncover novel AI applications that users find legitimate and useful, but also foster critical reflection on workplace practices and priorities. I conclude by outlining open challenges and a future research agenda for participatory AI systems and governance.

Bio: Min Kyung Lee is an assistant professor in the School of Information at the University of Texas at Austin. She has been a director of a Human-AI Interaction Lab since 2016. Dr. Lee has conducted some of the first studies that empirically examine the social implications of algorithms’ emerging roles in management and governance in society. She has extensive expertise in developing theories, methods and tools for human-centered AI and deploying them in practice through collaboration with real-world stakeholders and organizations. She developed a participatory framework that empowers community members to design matching algorithms that govern their own communities. Dr. Lee is a Siebel Scholar and has received the Allen Newell Award for Research Excellence, research grants from NSF, Ford Foundation, Omidyar Network, and Uptake, and ten best paper awards and honorable mentions and two demo/video awards in venues such as CHI, CSCW, DIS, HRI and MobiSys.

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 1:20PM - 2:30PM

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 2:45PM - 3:15PM

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.

The specific talks are as follows:

  • Explainability and Human Review as Procedural Fairness Safeguards in AI Proctoring for Educational Assessment (by Kwangsu Cho, Seonghyeon Park, and Kyuchan Oh)
  • Framework for Understanding the Sources of Bias in Artificial Intelligence (by Wolfgang Robinig and Johannes P. Wallner)
  • Who Gets Left Behind: Learning Style, AI Literacy Gaps, and Equity in Educational AI Chatbot Design (by Trang Xuan, Joni Salminen, Waleed Akhtar, Thanh Van Bui, Jack Tillotson, Soon-gyo Jung, and Bernard J. Jansen)

3. Coffee and refreshments break 3:15PM - 3:45PM

A coffee and refreshments break will be held during this time.

4. Participant talks 3:50PM - 4:45PM

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.

The specific talks are as follows:

  • Beyond Accuracy: Multimodal AI and Unequal Visibility of Learning Resources (by Ernesto William De Luca and Het Darshan Mehta)
  • Fairness in AI-Powered Surgical Assessment (by Chiu C. Tan, Ting Sun, Yongkai Wu)
  • Fairness Gaps in Educational AI Chatbots: A Cross-Country Usability Audit Across Gender, Culture, and Demographics Groups (by Trang Xuan, Joni Salminen, Waleed Akhtar, Thanh Van Bui, Youmen Chaaban, Naurin Farooq Khan, Haein Lee, Lu An, Wenjing Pian, Soon-gyo Jung, and Bernard J. Jansen)
  • AICInspector: A Lightweight Reliability Regulation Layer for Procedural Fairness in Generative AI Tutoring (by Yuyang Guo, Xianghui Meng, Jionghao Lin, Nancy Law)
  • Whose Default Voice? Cultural Fairness in Generative AI for Global South Learners (by Anguelina Popova)

5. Moderated panel 4:50PM - 5:50PM

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.

Organizers

  • Frank Stinar, University of Illinois Urbana–Champaign, USA
  • Chengyuan Yao, Columbia University, USA
  • Mirko Marras, University of Cagliari, Cagliari, Italy
  • Renzhe Yu, Columbia University, USA
  • Nigel Bosch, University of Illinois Urbana–Champaign, USA

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).

Related Workshops

We also invite you to check out the following related workshops:

Contacts

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