# XAI-ED > Hugo & Tailwindcss Starter -------------------------------------------------------------------------------- title: "Home" url: https://www.xai-ed.net/index.md -------------------------------------------------------------------------------- ## About the community **XAI-ED** is a community of researchers and practitioners working on **explainable and trustworthy AI and learning analytics in education**. As AI and learning analytics increasingly shape how people learn and teach, we work to ensure these systems are not only technically robust but also transparent, equitable, pedagogically aligned, and attentive to the needs of diverse stakeholders. We are an interdisciplinary community spanning educational data mining, learning analytics, human–computer interaction, cognitive science, and machine learning. We ask how the outcomes of educational AI can be explained to the people they affect — learners, teachers, and institutions — and how explainability can build trust, support good decisions, and keep humans in control. The community comes together through workshops at venues such as Educational Data Mining (EDM), Artificial Intelligence in Education (AIED), the International Conference on Learning Analytics & Knowledge (LAK), and the Festival of Learning. Explore the [editions](/editions/) to see past and upcoming events, or get to know the [people behind XAI-ED](/who-we-are/). -------------------------------------------------------------------------------- title: "Contact" url: https://www.xai-ed.net/contact/index.md description: this is meta description -------------------------------------------------------------------------------- this is meta description -------------------------------------------------------------------------------- title: "News" url: https://www.xai-ed.net/blog/index.md description: News and announcements from the XAI-ED community. -------------------------------------------------------------------------------- News and announcements from the XAI-ED community. -------------------------------------------------------------------------------- title: "Meet us at the Festival of Learning (AXLE)" url: https://www.xai-ed.net/blog/meet-us-at-festival-of-learning-axle/index.md date: "2026-06-24" description: The AXLE workshop brings XAI-ED to the Festival of Learning 2026 — come and meet the community. -------------------------------------------------------------------------------- Our next stop is **AXLE — the XAI-ED workshop at the Festival of Learning 2026**. Come along to meet the community, hear about explainable AI in education, and join the conversation. Find the details and visit the workshop site on the [AXLE edition page](/editions/axle/). -------------------------------------------------------------------------------- title: "The new XAI-ED website is online" url: https://www.xai-ed.net/blog/xai-ed-website-is-online/index.md date: "2026-06-24" description: The XAI-ED community now has a home on the web, bringing all our workshops and editions together in one place. -------------------------------------------------------------------------------- The new **XAI-ED community website** is up and running. This is the home of the XAI-ED community — explainable AI in education — bringing together our workshops and editions in one place. Take a look around the [editions](/editions/) and get to know the [people behind it](/who-we-are/). More is on the way as the community grows. -------------------------------------------------------------------------------- title: "Authors" url: https://www.xai-ed.net/authors/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "XAI-ED" url: https://www.xai-ed.net/authors/xai-ed/index.md description: News and announcements from the XAI-ED community. -------------------------------------------------------------------------------- The XAI-ED community. -------------------------------------------------------------------------------- title: "Editions" url: https://www.xai-ed.net/editions/index.md description: XAI-ED workshops and events. Each edition has its own site. -------------------------------------------------------------------------------- XAI-ED workshops and events. Each edition has its own site. -------------------------------------------------------------------------------- title: "AXLE — Festival of Learning 2026" url: https://www.xai-ed.net/editions/axle/index.md -------------------------------------------------------------------------------- AXLE: Agency-driven eXplainable Learning Experiences — reframing explainability as a design challenge for accountable, equitable, and agency-supportive AI in education, at the Festival of Learning 2026. -------------------------------------------------------------------------------- title: "XAI-ED @ LAK26" url: https://www.xai-ed.net/editions/lak26/index.md -------------------------------------------------------------------------------- Third workshop on Explainable AI in Education at the 16th International Conference on Learning Analytics & Knowledge (LAK 2026) — demystifying AI in education through explainability, agency, and transparency. -------------------------------------------------------------------------------- title: "Sections" url: https://www.xai-ed.net/sections/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "Who We Are" url: https://www.xai-ed.net/who-we-are/index.md description: The people behind the XAI-ED community. -------------------------------------------------------------------------------- The people behind the XAI-ED community. -------------------------------------------------------------------------------- title: "Benjamin Paaßen" url: https://www.xai-ed.net/who-we-are/benjamin-paassen/index.md description: this is meta description -------------------------------------------------------------------------------- Benjamin Paaßen is junior professor for knowledge representation and machine learning at Bielefeld University. Their research focuses on interpretable, explainable, and domain-informed machine learning, especially for intelligent tutoring systems. As part of the large-scale research projects SAIL, KI-Akademie OWL, and the collaborative research center TRR318 "Constructing Explainability", they engage both in foundational research as well as science communication to explain opportunities as well as limitations of contemporary AI systems. -------------------------------------------------------------------------------- title: "Christian Weber" url: https://www.xai-ed.net/who-we-are/christian-weber/index.md description: this is meta description -------------------------------------------------------------------------------- Dr. Christian Weber is leading the research group Medical Informatics and Graph-based Systems (.MIGS), jointly with Prof. Dr.-Ing. Kai Hahn in the faculty of Science and Technology at the University of Siegen. Christian Weber’s research focuses on Graph-based Systems, Recommender Systems, and AI, which he is applying in medicine, education, and manufacturing. He represented the professorship of Medical Data Science from 2022 till 2024 at the University of Siegen. He acquired his PhD from the Corvinus University Budapest in Hungary, as part of a Marie Skłodowska-Curie Actions Doctoral Network, where he laid new foundations for knowledge intense, individualized learning path recommendations for vocational educational training in medical and industrial applications. -------------------------------------------------------------------------------- title: "Hasan Abu-Rasheed" url: https://www.xai-ed.net/who-we-are/hasan-abu-rasheed/index.md description: this is meta description -------------------------------------------------------------------------------- Hasan Abu-Rasheed is a postdoctoral researcher in the field of artificial intelligence in education, with a particular focus on explainable AI (XAI), knowledge graphs, and semantic technologies. He is conducting his research at Goethe University Frankfurt, Germany, where he contributes to the development of AI-driven tools for higher education, ranging from intelligent dialogue systems (chatbots) to agent-based workflows for semantic information extraction and explainable learning analytics and feedback. -------------------------------------------------------------------------------- title: "Hassan Khosravi" url: https://www.xai-ed.net/who-we-are/hassan-khosravi/index.md description: this is meta description -------------------------------------------------------------------------------- Associate Professor Hassan Khosravi is a recognised leader in Data Science and Artificial Intelligence in Education at The University of Queensland. His research sits at the intersection of learning sciences and human–computer interaction, with a particular focus on advancing the responsible and ethical use of AI in education. He has taught more than 15,000 students across a wide range of courses, authored over 100 peer-reviewed publications, and secured more than $6 million in competitive research funding. His contributions are shaping both scholarly discourse and practical innovation on the transformative role of AI in education. -------------------------------------------------------------------------------- title: "Jakub Kuzilek" url: https://www.xai-ed.net/who-we-are/jakub-kuzilek/index.md description: this is meta description -------------------------------------------------------------------------------- Jakub Kuzilek is affiliated as a researcher/research software engineer with the Learning Science in Higher Education research group at FernUniversität in Hagen. His research focuses on learning environments powered by machine learning systems development, explainable machine learning in education and predictive analysis of student behaviour in online environments. -------------------------------------------------------------------------------- title: "Jeroen Ooge" url: https://www.xai-ed.net/who-we-are/jeroen-ooge/index.md description: this is meta description -------------------------------------------------------------------------------- Dr. Jeroen Ooge is an assistant professor at Utrecht University. His research is situated in human-computer interaction, which investigates how people interact with technologies. Dr. Ooge is specialised in human-centred explainable artificial intelligence, which studies how outcomes of AI models can be explained to different audiences in different contexts; for example, teenagers in an educational context. He is interested in how transparency affects people's trust in AI models, how it supports people's decision-making, whether it improves people's understanding of AI models, etc. with particular focus on studying how data visualisation can help in this endeavour -------------------------------------------------------------------------------- title: "Juan D. Pinto" url: https://www.xai-ed.net/who-we-are/juan-pinto/index.md description: this is meta description -------------------------------------------------------------------------------- Juan D. Pinto is a PhD student at the University of Illinois Urbana‐Champaign. His research involves the development of learner models using machine learning methods and tackling issues of AI interpretability in education. He is currently conducting work as a member of the Human‐centered Educational Data Science (HEDS) Lab and the NSF AI Institute for Inclusive Intelligent Technologies for Education (INVITE). -------------------------------------------------------------------------------- title: "Lea Cohausz" url: https://www.xai-ed.net/who-we-are/lea-cohausz/index.md description: this is meta description -------------------------------------------------------------------------------- Lea Cohausz is a PhD student at the University of Mannheim. Her recent work includes research on how demographic variables influence predictions in EDM and the consequences for fairness (EDM 2023) as well as identifying causal structures in educational data and their relationship to algorithmic bias (LAK 2024). She is interested in advancing our understanding of the complex relationships of factors that influence students’ learning outcomes. -------------------------------------------------------------------------------- title: "Luc Paquette" url: https://www.xai-ed.net/who-we-are/luc-paquette/index.md description: this is meta description -------------------------------------------------------------------------------- Luc Paquette is an associate professor in the department of curriculum \& instruction at the University of Illinois Urbana-Champaign. His research focuses on the usage of machine learning, data mining and knowledge engineering approaches to analyze and build predictive models of the behavior of students as they interact with digital learning environments such as MOOCs, intelligent tutoring systems, and educational games. He is interested in studying how those behaviors are related to learning outcomes and how predictive models of those behaviors can be used to better support the students' learning experience. -------------------------------------------------------------------------------- title: "Luca Longo" url: https://www.xai-ed.net/who-we-are/luca-longo/index.md description: this is meta description -------------------------------------------------------------------------------- Dr. Luca Longo received the bachelor’s andmaster’s degrees in computer science, an MSc in statistics and one in health informatics, and a Ph.D. in artificial intelligence from Trinity CollegeDublin. He also earned two MSc degrees in pedagogy from Technological University Dublin. He is currently the leader of the Artificial Intelligenceand Cognitive Load Research Laboratories and the director of the Explainable Artificial Intelligence Centre. With his team of doctoral and postdoctoral scholars, he conducts fundamental research in eXplainable Artificial Intelligence, defeasible reasoning, and non-monotonic argumentation. He also performs applied research in deep learning and neuroscience, mainly applied to the problem of mental workload modelling using electroencephalography. He is also the Founder of the World Conference on eXplainable Artificial Intelligence. He actively disseminates scientific material to the public, contributing to the non-profit TED Organisation. -------------------------------------------------------------------------------- title: "Mutlu Cukurova" url: https://www.xai-ed.net/who-we-are/mutlu-cukurova/index.md description: this is meta description -------------------------------------------------------------------------------- Prof. Mutlu Cukurova is affiliated with UCL Knowledge Lab at the Institute of Education and UCL Centre for Artificial Intelligence at the Faculty of Engineering at University College London. He investigates human-AI complementarity in teaching and learning contexts and leads the UCL Learning Analytics and AI in Education group at UCL Knowledge Lab. He is engaged in policy-making activities as an external expert for UNESCO, OECD, and EC authoring numerous influential policy reports (e.g. UNESCO AI competency framework for teachers and Teachers agency in the age of AI). He was the programme co-chair of the International Conference of AI in Education in 2020 and is named in Stanford’s Top 2% Scientists List. He is also Editor-in-Chief of the British Journal of Educational Technology and Associate Editor of the International Journal of Child–Computer Interaction. -------------------------------------------------------------------------------- title: "Qianhui (Sophie) Liu" url: https://www.xai-ed.net/who-we-are/sophie-liu/index.md description: this is meta description -------------------------------------------------------------------------------- Qianhui (Sophie) Liu is a PhD student at the University of Illinois Urbana-Champaign. Her research in the HEDS lab focuses on applying data mining methods in combination with learning science theories to help improve the efficiency of teaching and learning in various educational settings. She is interested in closing the loop of machine learning to humans for actionable insights through explainable models and techniques. -------------------------------------------------------------------------------- title: "Tanja Käser" url: https://www.xai-ed.net/who-we-are/tanja-kaeser/index.md description: this is meta description -------------------------------------------------------------------------------- Tanja Käser is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the Machine Learning for Education (ML4ED) laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning, with a focus on building models that are generalizable, interpretable, and fair. -------------------------------------------------------------------------------- title: "Vinitra Swamy" url: https://www.xai-ed.net/who-we-are/vinitra-swamy/index.md description: this is meta description -------------------------------------------------------------------------------- Vinitra Swamy is a postdoctoral researcher at EPFL. Her research with the ML4ED lab involves explainable AI for education, especially through the lens of reducing adoption barriers for neural networks. Her recent work focuses on uncovering disagreement in post-hoc explainers, using learning science experts to validate explainer accuracy and actionability, and proposing interpretable-by-design neural network architectures. -------------------------------------------------------------------------------- title: "Categories" url: https://www.xai-ed.net/categories/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "Announcement" url: https://www.xai-ed.net/categories/announcement/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "Tags" url: https://www.xai-ed.net/tags/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "AXLE" url: https://www.xai-ed.net/tags/axle/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "Community" url: https://www.xai-ed.net/tags/community/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "Festival of Learning" url: https://www.xai-ed.net/tags/festival-of-learning/index.md -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- title: "Website" url: https://www.xai-ed.net/tags/website/index.md --------------------------------------------------------------------------------