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Paper submission deadline extended to 15th of June2025
In the 26th International Conference on Artificial Intelligence in Education
(AIED 2025)
on July 22, 2025, between 9:00h - 13:00h
Palermo, Italy
This year, we are pleased to announce the collaboration between the XAI-Ed workshop and the 2nd Human-Centric eXplainable AI in Education (HEXED) workshop, which will be held in conjunction with the Educational Data Mining (EDM) 2025 conference, which is jointly located this year with AIED in Palermo.
This partnership reflects a shared commitment to advancing the field of XAI within educational contexts. Therefore, we strongly encourage participants in XAI-Ed to consider attending HEXED workshop to get a thorough complement to the content of XAI-Ed.
In support of this collaborative spirit, XAI-Ed and HEXED will jointly publish their workshop proceedings, promoting further integration and dialogue across both communities in addressing the evolving needs and challenges in human-centered, and pedagogy-founded XAI for education.
Why XAI-Ed Workshop
As educational technology evolves, integrating Artificial Intelligence (AI) into Technology-Enhanced Learning (TEL), Educational technologies (EdTech), and AI in Education (AIED) offers significant potential for learning and the educational stakeholders. However, the opacity of many AI systems complicates the interpretation of their decision making processes, and their predictions of, e.g., personalized learning recommendations. Explainable AI (XAI) addresses this challenge by providing insights into how AI algorithms arrive at their predictions, thereby enabling stakeholders to make more informed decisions. This workshop will explore the convergence of XAI with AIED, TEL, and EdTech in general, aiming to empower learners and educators through transparent and understandable insights into the inner workings of AI algorithms.
As educational technology evolves, integrating Artificial Intelligence (AI) into Technology-Enhanced Learning (TEL), Educational technologies (EdTech), and AI in Education (AIED) offers significant potential for learning and the educational stakeholders. However, the opacity of many AI systems complicates the interpretation of their decision making processes, and their predictions of, e.g., personalized learning recommendations. Explainable AI (XAI) addresses this challenge by providing insights into how AI algorithms arrive at their predictions, thereby enabling stakeholders to make more informed decisions. This workshop will explore the convergence of XAI with AIED, TEL, and EdTech in general, aiming to empower learners and educators through transparent and understandable insights into the inner workings of AI algorithms.
Although the recent advances within the AIED community underscored the growing demand for explainable and transparent AI systems, the focus has mainly been on the technical side of XAI, leaving a strong need for addressing the pedagogical aspects of XAI in education. Those are not only limited to the pedagogical foundations of XAI but also include the actual learning value from implementing XAI, how this value aligns with learning theories, and how XAI’s ability to achieve it can be evaluated. In educational contexts, where learner autonomy and agency are essential for a fair, learner-centered process, XAI is expected to provide meaningful explanations for algorithmic predictions. Successfully implementing XAI in EdTech and AIED, therefore, requires a robust understanding of its pedagogical foundations, technical realization, regulatory limitations, and the perspectives of the educational stakeholders, including learners, educators, developers, and institutions.
Although the recent advances within the AIED community underscored the growing demand for explainable and transparent AI systems, the focus has mainly been on the technical side of XAI, leaving a strong need for addressing the pedagogical aspects of XAI in education. Those are not only limited to the pedagogical foundations of XAI but also include the actual learning value from implementing XAI, how this value aligns with learning theories, and how XAI’s ability to achieve it can be evaluated. In educational contexts, where learner autonomy and agency are essential for a fair, learner-centered process, XAI is expected to provide meaningful explanations for algorithmic predictions. Successfully implementing XAI in EdTech and AIED, therefore, requires a robust understanding of its pedagogical foundations, technical realization, regulatory limitations, and the perspectives of the educational stakeholders, including learners, educators, developers, and institutions.
Workshop Goals
Foster interdisciplinary dialogue on XAI in education.
Promote the design and implementation of XAI with strong pedagogical foundations.
Encourage user-centered co-design between technical, educational, and regulatory stakeholders, to empower learners and educators through transparent AI decision-making.
Share tools, methods, and evaluation strategies for implementing XAI.
Address ethical and legal considerations in XAI for education.
📢 Acepted Papers
The second annual XAI-Ed Workshop explores the intersection of Explainable AI (XAI), education, and human-centered design, with a strong focus on pedagogical foundations, stakeholder agency, ethical implications, and interdisciplinary collaboration.
The second annual XAI-Ed Workshop explores the intersection of Explainable AI (XAI), education, and human-centered design, with a strong focus on pedagogical foundations, stakeholder agency, ethical implications, and interdisciplinary collaboration.
In this workshop orund, we are glad to announce 7 accepted papers:
In this workshop orund, we are glad to announce 7 accepted papers:
Trustable AI: Explain the Values of an Open Learner Model (Maeva Somny, Marie Lefevre and Nathalie Guin)
Exploring Complex Connections Between Explainable AI, Pedagogy and Individual AI Competencies in Education (Joy Laue Christensen)
Explainable Collaborative Problem Solving Diagnosis with BERT using SHAP and its Implications for Teacher Adoption (Yew Chong Kester Wong, Sahan Bulathwela and Mutlu Cukurova)
Designing Explainability Features for LLM-based Educational Chatbots to Promote Reflective Learning Behavior (Iulia Costea and Gayane Sedrakyan)
When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard (Angxuan Chen)
Addressing Teacher Resistance Critical Factors with XAI (Airina Volungevičienė and Patricija Škėmaitė)
Educational Stakeholders Need A Legal Guide on Explainable AI (Lezel Roddeck, Hasan Abu-Rasheed, Jeroen Ooge and Francisco Bellas)
Program at a Glance
- Welcome & overview
- Presentation on XAI principles in education
- Discussion on stakeholder roles and contexts
- Paper presentations
- Break
- Panel discussion
- Presentation on XAI methods and tools
- Invited talk
- Oopen discussion, reflection and closing
Detailed workshop program can be found here
Workshop Organizers
Program Committee
Olga Viberg, KTH Royal Institute of Technology.
Paul Prinsloo, University of South Africa.
Gabriella Casalino, University of Bari Aldo Moro.
Irene-Angelica Chounta, University of Duisburg-Essen.
Francisco Javier Bellas, Universidade da Coruña.
Jordan Barría Pineda, University of Pittsburgh.
Yifei Gao, University College London.
Venue
The XAI-Ed workshop will be held within the AIED2025 conference at the Conference Venue of the University of Palermo, Palermo, Italy.
Let us know if you'll be attending! (optional)
Let us know if you'll be attending! (optional)
We would like to know more about you, to ensure that the workshop meets your goals and expectations.
Contact
For any question or inquiry, do not hesitate to contact us via:
Hasan Abu-Rasheed: rasheed@studiumdigitale.uni-frankfurt.de
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