Pre-conference Schedule
*Please note that all workshops are subject to change and cancellation due to low enrollments. If your workshop is cancelled, your workshop registration will be refunded. All other refunds will follow the LAK26 refund policy found on the registration page.
*All LAK26 events are fully in-person.
*Some LAK workshops accept submissions, abstracts, problems to solve or other types of submission that provides additional ways to participate in LAK26 and be an integral part of the workshop. Any LAK26 workshop that is accepting any type of submission is labeled with ** next to their title and noted within the workshop description text. Submission information can be found on their respective websites. Submission deadlines for workshop papers is December 4, 2025.
- Please note that all are still welcome to register and attend ANY workshop - submission is not a requirement to attend.
Monday, April 27, 2026 - Full Day | 9:00 AM to 5:00 PM | In-Person
Type: Mini-Track Symposium (Accepting submissions)
Description:
Tutoring is one of the most effective interventions in education, yet the specific instructional moves that make tutoring so impactful remain underexplored at scale. The National Tutoring Observatory (NTO) is creating shared infrastructure—including the Million Tutor Moves dataset and AI-assisted annotation pipelines—to help researchers capture, segment, and analyze multimodal tutoring interactions linked to student outcomes. This workshop will introduce the NTO’s mission and tools, and provide LAK participants with opportunities to engage directly with its annotation workflows. The format combines demonstration, hands-on analysis, and community presentations. Participants will first be introduced to the NTO’s goals and infrastructure, followed by a live demo of annotation tools. In small groups, attendees will then experiment with annotating their own datasets or sample data. Researchers will present short papers on early work conducted with NTO resources. This parallel structure ensures that participants both generate new analyses and engage with emerging findings during the workshop itself. The event will conclude a discussion of insights, challenges, and next steps based on the presented research and experiences in the interactive session. By fostering interdisciplinary collaboration, this workshop aims to strengthen the LAK community’s role in advancing the science of tutoring and teaching.
Organizers:
Kirk Vanacore, Cornell University
Rene Kizilcec, Cornell University
Rachel Slama, Cornell University
Joshua Marland, National Tutoring Observatory
Bakhtawar Ahtisham, National Tutoring Observatory
Zhuqian Zhou, National Tutoring Observatory
Danielle Thomas, Carnegie Mellon University
Kenneth Koedinger, Carnegie Mellon University
Doug Pietrzak, FreshCognate LLC
Justin Reich, MIT
Workshop Website: https://sites.google.com/cornell.edu/lak26-nto-workkshop/home
Type: Mini-Track Symposium (Accepting Submissions)
Description:
TNA is a novel framework for modeling the learning process through a rich toolkit of rigorous, statistically validated methods. TNA provides an accessible yet powerful environment for researchers to perform rigorous analyses—without requiring coding expertise. TNA supports the computation of metrics at the network, node, and edge levels, facilitating the identification of recurring patterns, such as dyads, triads, communities, and clusters. TNA integrates statistical validation techniques —including bootstrapping, permutation testing, and case-dropping— to assess the robustness and replicability of findings. This allows researchers to statistically validate each edge in the network with both p-values and effect sizes —an unprecedented capability in process modeling. Furthermore, TNA enables comparison across subgroups and the explanation of observed patterns with edge-level statistical significance. The workshop’s learning objectives include helping participants develop a solid understanding of theoretical foundations and methodological affordances of TNA and its key variants: Frequency-based TNA, Attention Network Analysis, and Co-occurrence TNA. Attendees will learn how to identify appropriate data and research questions for TNA, perform necessary data preprocessing, and apply the method using both code-based tools (tna R package) and no-code platforms (tna-web and JTNA).
Organizers:
Mohammed Saqr, University of Eastern Finland
Kamila Misiejuk, FernUniversität in Hagen
Vokan Yücepur, FernUniversität in Hagen
Sonsoles López-Pernas, University of Eastern Finland
Workshop Website: https://sites.uef.fi/learning-analytics/tna-lak-workshop-2026/
Type: Mini-Track Symposium (Accepting Submissions)
Description:
As artificial intelligence (AI) becomes increasingly embedded in educational technologies and learning analytics (LA), the demand for transparency, trust, and fairness has never been greater. Integrating explainability into LA, however, remains relatively underexplored. Meanwhile, concerns persist around black-box approaches to analytics, which lack transparency and interpretability. This workshop addresses the critical role of explainable AI (XAI) in education, positioning it as a means to bridge the gap between complex algorithmic predictions and the needs of students, teachers, and institutional leaders. By combining technical advances with pedagogical principles, the workshop emphasizes explanation faithfulness, stakeholder-sensitive design, and actionable insights. It highlights the importance of moving towards explainable learning analytics (XLA) as an area to foreground and further develop. Participants will explore approaches ranging from intrinsic and post-hoc explainability to using large language models for adaptive explanations. Ethical and legal considerations, including issues of bias, accountability, and compliance, will be central to the discussions. Through interactive sessions, paper presentations, and collaborative activities, the workshop aims to foster interdisciplinary dialogue, share evaluation frameworks and tools, and co-create strategies for institutional adoption. Ultimately, it seeks to strengthen the role of XAI in empowering stakeholders, supporting equitable learning opportunities, and enhancing LA's impact in education.
Organizers:
Hasan Abu-Rasheed, Goethe University
Lea Cohausz, University of Mannheim
Mutlu Cukurova, University College London
Tanja Kaeser, EPFL
Hassan Khosravi, The University of Queensland
Jakub Kuzilek, FernUniversität in Hagen
Luca Longo, Research Centre of eXplainable Artificial Intelligence
Jeroen Ooge, Utrecht University
Juan Pinto, University of Illinois Urbana‐Champaign
Vinitra Swamy, EPFL
Christian Weber, University of Siegen
Luc Paquette, University of Illinois Urbana-Champaign
Sophie Qianhui Liu, University of Illinois Urbana-Champaign
Workshop Website: https://www.xai-ed.net/
More information can be found on the AI Agents Academy page: https://www.solaresearch.org/events/lak/lak26/ai-agents-academy/
Monday, April 27, 2026 - AM Half Day | 9:00 AM to 12:30 PM | In-Person
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Building on the momentum of the previous GenAI-LA workshops at LAK24 and LAK25, the Third International Workshop on GenAI-LA sharpens its focus on the evolving dynamics of human-AI collaboration in education. Past editions sparked vibrant conversations and fostered practical collaborations. Attendance has been strong, with more than 110 participants in the inaugural event and 23 workshop papers. The third workshop will delve deeply into the collaborative relationships emerging between humans and GenAI tools in learning environments, aiming to generate fresh insights into how these partnerships can be best understood, measured, and designed. Central to this edition is the exploration of how learning analytics can illuminate, shape, and optimise the dynamics of human-AI collaboration, not just its impacts, but its underlying processes, affordances, and challenges. We invite researchers, practitioners, and designers to contribute empirical studies, methodological innovations, and critical perspectives that advance understanding in this rapidly developing field.
Organizers:
Lixiang Yan (Tsinghua University)
Yueqiao Jin (Monash University)
Andy Nguyen (University of Oulu)
Wanruo Shi (Tsinghua University)
Ryan S. Baker (University of Pennsylvania)
Mutlu Cukurova (University College London)
Dragan Gašević (Monash University)
Nancy Law (University of Hong Kong)
Yuheng Li (The Hong Kong Polytechnic University)
Workshop Website: https://sites.google.com/monash.edu/genai-la-workshop-lak/home
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Large Language Models (LLMs) are increasingly used for tasks traditionally performed by human learners, such as reading, writing, problem solving, and programming. While current evaluations rely heavily on benchmarks, mature frameworks from educational measurement – such as Item Response Theory (IRT), cognitive diagnostic models (e.g., DINA), and learning taxonomies – offer principled approaches for understanding their capabilities and limitations. This workshop will explore how these theories can inform the evaluation of LLMs and human-AI collaboration, highlight divergences and alignments with human learning processes, and address concerns around responsible AI use in education.
Organizers:
Giora Alexandron, Weizmann Institute of Science
Beata Beigman Klebanov, Educational Testing Service
Jill Burstein, Duolingo
Yang Jiang, Educational Testing Service
Alona Strugatski, Weizmann Institute of Science
Licol Zeinfeld, Weizmann Institute of Science
Workshop Website: https://llm-psychometrics-lak26-plum.vercel.app/#main
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Personalized learning analytics (LA) often default to population‑level models that overlook meaningful individual differences in cognition, motivation, prior knowledge, and strategy use. Building on calls to integrate theory and focus on learners as persons rather than averages, this half‑day workshop convenes researchers and practitioners to co‑design theory‑informed approaches to personalization. We will (a) map core constructs from learning sciences and psychology (e.g., self‑regulated learning, expectancy–value, cognitive load, goal orientation, and mindset) to measurable LA indicators; (b) examine idiographic/within‑person models as complements to aggregate prediction; and (c) formulate ethical, transparent workflows for tailoring feedback and support. The format blends a short keynote, lightning talks, and guided small‑group design sprints culminating in a shared roadmap and artifacts. Participants will be invited to contribute short, peer‑reviewed extended abstracts. This workshop advances LAK’s agenda by grounding personalization in theory and demonstrating concrete tools and methods educators can adopt.
Organizers:
Laura Brandl, LMU Munich
Oleksandra (Sasha) Poquet, Technical University of Munich (TUM)
Matthias Stadler, LMU University Hospital, Munich
Workshop Website: https://sites.google.com/view/lakworkshop/home
Type: Interactive Workshop Session
Description:
This workshop is fourth in the series of InnovateDesign workshops, which have introduced the LAK community to the innovative concept of learning design (LD) and a complimentary tool for creating and analyzing LD, provided a platform to discuss AI's role in LD, as well as the intersection of learning analytics (LA) and AI-supported LD. To provide a background for a further discussion on LD, LA and AI, editors of the special issue of the International Journal of Educational Technology in Higher Education will give an overview of the research presented in the special issue. After that, examples of LDs of courses and a study program will be presented to demonstrate and encourage discussion on the possible use of LD data. The discussion will include, for example, the use of LD data to analyse learning paths and patterns, and recognize the quality of LD, both on the level of a course and a study program (curriculum analytics). Finally, participants will do some hands-on work in the LD tool and provide their feedback. This half-day, in-person workshop is a collaborative effort by four esteemed universities from Europe, Australia and USA.
Organizers:
Darko Grabar, University of Zagreb, Faculty of Organization and Informatics, Croatia
Barbi Svetec, University of Zagreb, Faculty of Organization and Informatics, Croatia
Petra Vondra, University of Zagreb, Faculty of Organization and Informatics, Croatia
Dragan Gašević, Monash University, Faculty of Information Technology, Australia
Mladen Raković, Monash University, Faculty of Information Technology, Australia
Bart Rienties, The Open University, Institute of Educational Technology, United Kingdom
Thomas Penniston, University of Maryland, Baltimore County, USA
Workshop Website: https://learning-design.eu/en/lak-2026
Type: Interactive Workshop Session
Description:
This workshop will focus on the need to embed reflection and critical thinking as central components of workplace learning and how Learning Analytics can support these processes. At the intersection of industry and research, the workshop will explore how instructional approaches can go beyond technical training to cultivate adaptive expertise, critical digital literacies, and thoughtful decision-making. Participants will engage in activities and discussions about how reflective practice and critical inquiry, aided by Learning Analytics (LA), can be integrated into upskilling and reskilling programs, ensuring that learners embrace new skills and are able to question, evaluate, and shape their impact. By foregrounding these instructional elements, the workshop underscores the role of reflective and critical capacities in building resilient, future-ready learning ecosystems.
Organizers:
Bruce M. McLaren, Carnegie Mellon University, U.S.A.
Olga Viberg, KTH Royal Institute of Technology, Sweden
Maren Scheffel, Ruhr-Universität Bochum, Germany
Workshop Website: https://www.cs.cmu.edu/~bmclaren/mclearnlab/lak-2026
Type: Interactive Workshop Session
Description:
This half-day workshop introduces participants to advanced applications of Quantitative Ethnography (QE) in the context of AI-augmented learning environments. Building on previous LAK workshops, it highlights how QE weaves together the depth of qualitative insights and the breadth of quantitative patterns to make sense of complex learning data shaped by AI systems. Participants will gain hands-on experience with established and emerging QE tools, including Epistemic Network Analysis (ENA), Ordered Network Analysis (ONA), and Transmodal Analysis (TMA), alongside Codey, a new LLM-powered tool for automated and validated coding. Designed for both newcomers and intermediate users, the session combines theoretical grounding with practical exploration of how QE supports theory-driven, multimodal, and process-oriented learning analytics. Through guided activities and collaborative discussions, attendees will strengthen their methodological toolkit for analyzing human–AI interactions and interpreting learning processes with rigor, transparency, and contextual richness.
Organizers:
Zachari Swiecki, Monash University, Australia
Kamila Misiejuk, FernUniversität in Hagen, Germany
Rogers Kaliisa, University of Oslo, Norway
Mamta Shah, Elsevier, United States
Brendan Eagan, UW Madison, United States
Yuanru Tan, UW Madison, United States
Cody Marquart, UW Madison, United States
Workshop Website: https://www.epistemicanalytics.org/2025/10/21/lak26-workshop-quantitative-ethnography-in-the-age-of-ai-advanced-tools-and-methods-for-learning-analytics/
Monday, April 27, 2026 - PM Half Day | 1:30 PM to 5:00 PM | In-Person
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Following the success of the inaugural workshop, this second iteration continues to serve as a forum for advancing dialogue and fostering collaborations among researchers and practitioners interested in learning analytics for Hybrid Intelligence. The inaugural workshop brought together 39 participants and resulted in the publication of seven peer-reviewed workshop papers. Over the past year, notable advancements have been achieved, and the concept of Hybrid Intelligence, referring to the integration of human and artificial intelligence, has attracted growing interest across various disciplines, particularly within learning sciences, learning analytics, and human-computer interaction. The workshop will explore the mutual shaping of human-AI collaboration and learning, with a particular emphasis on how learning analytics can inform and enhance hybrid intelligence systems, and conversely, how hybrid intelligence can enrich the scope and impact of learning analytics. The workshop aims to advance the conceptual, methodological, and design foundations of Hybrid Intelligence by bringing together a transdisciplinary community of learning scientists, learning analytics researchers, software engineers, and AI experts. Participants will engage in critical discussions, share emerging work, and identify future directions for research and development at the intersection of human learning and intelligent systems.
Organizers:
Andy Nguyen (University of Oulu)
Sanna Järvelä (University of Oulu)
Kshitij Sharma (Norwegian University of Science and Technology)
Michail Giannakos (Norwegian University of Science and Technology)
Alyssa Wise (Vanderbilt University)
Carolyn Rose (Carnegie Mellon University)
Mutlu Cukurova (University College London)
Lixiang Yan (Tsinghua University)
Luna Huynh (University of Oulu)
Workshop Website: https://sites.google.com/view/hilak2026/home
Type: Interactive Workshop Session
Description:
The first five editions of the Workshop on Learning Analytics and Assessment were successfully organized at LAK21-25 conferences, resulting in
multiple post-workshop collaborations and a special issue in a journal. In this workshop, we intend to address some of the key open challenges in learning analytics that are related to use of learning analytics in formative and summative assessment; measurement of learning progression; reliability and validity of data collection and analysis; and assurance of assessment trustworthiness, in particular given the emergence of the generative artificial intelligence (AI) methods. An open call for contributions will be distributed to solicit brief descriptions of current research and practice projects for roundtable-style discussions with workshop participants. Expected outcomes are the further formation of a community of practice and possible follow-up publications and special issues in journals.
Organizers:
Dragan Gasevic, Monash University
Mladen Rakovic, Monash University
Blazenka Divjak, Zagreb University
Yoon Jeon Kim, Univesity of Wisconsin-Madison
Abhinava Barthakur, University of South Australia
Zhichun Liu, University of Hong Kong
Workshop Website: https://sites.google.com/monash.edu/lakassess26/about
Type: Interactive Workshop Session
Description:
Actionable learning analytics requires causal claims; to take well-informed action implies we have an understanding of the causal effect of that action. Causal claims typically require a warrant from outside of the data, built on principled scientific reasoning. Causal directed acyclic graphs (DAGs) are a way to draw causal assumptions of a system to support causal claims from domain expert knowledge. This workshop will introduce participants to drawing such graphical causal models, both from domain experts and data, and how DAGs can be used to think about and exploit the causal assumptions underlying learning analytics systems. The workshop will build a proposal for future research in this emerging field to better align the rich domain knowledge of learning systems with the rapidly evolving learning analytics tools, processes, and AI.
Organizers:
Ben Hicks, New South Wales Department of Education
Lea Cohausz, University of Mannheim
Nigel Bosch, University of Illinois Urbana-Champaign
Joshua Weidlich, University of Zurich
Workshop Website: https://sites.google.com/view/lak26-workshop-gcm-for-la/home
Type: Interactive Workshop Session
Description:
Learning analytics offers tremendous promise to understanding and enhancing student engagement in educational settings, ultimately improving educational outcomes. However, innovative new measures and metrics designed to capture the multifaceted nature of engagement—whether behavioral, emotional, social, or cognitive—often remain isolated within individual institutions or projects. Building on the momentum of the successful Workshop on New Measures & Metrics (LAK24 and LAK25), this half-day event aims to collaboratively advance the development and dissemination of metrics relevant to K-12 and post-secondary education. This year’s workshop will spotlight submissions of engagement-related measures that move beyond conceptual descriptions to demonstrate their application in real learning environments. Selected measures will be compiled on a website and presented at the event. Whether derived from clickstream data, affective indicators, or multimodal sources, these metrics will help capture the complexity of student engagement and highlight exciting new LA methods across contexts. Through mini-presentations, structured discussion, and breakout sessions, participants will exchange insights about creating, validating, and distributing innovative student engagement metrics. By synthesizing diverse methodological approaches, the workshop aims to inform new learning analytic techniques that can be generalized and adopted broadly. This interactive workshop provides an opportunity to collectively spur progress of the next generation of learning metrics.
Organizers:
Charles Lang, Teachers College Columbia University
Geraldine Gray, TU Dublin
Ruth Cobos, Universidad Autonoma de Madrid Detra Price-Dennis, Ohio State University
Jae H. Paik, San Francisco State University
Lujie Karen Chen, University of Maryland Baltimore County
Jie Gao, McGill University
Xiaomeng Huang, NYU
Workshop Website: https://charles-lang.github.io/measures-metrics-LAK26/
Type: Interactive Workshop Session
Description:
This interactive workshop provides a hands-on introduction to high-quality, freely available learning analytics curricula that can be utilized by faculty and researchers worldwide. Developed for the LASER Institute with funding provided by the United States’ National Science Foundation (ECR: BCSER), the materials shared during this workshop were created to be utilized by early- and mid-career scholars who incorporate learning analytics techniques into their teaching and research. This workshop will guide attendees through the LASER BEAM (Broadening Education in Advanced Methods) curriculum materials and aid them in adapting these materials for use at their home institutions or schools via webinars, workshops, courses, or other programs. Participants will learn the fundamental design and structure of over two dozen learning modules, including: learning analytics workflow, supervised machine learning, social network analysis, text mining, knowledge tracing, and relationship mining. Participants will gain hands-on experience with instructional materials and their supporting technology, including: conceptual overview presentations, interactive code-alongs and case studies using R and Python, essential readings and discussion activities, and badging and microcredential options. Additionally, pedagogically-based discussion will focus on helping participants incorporate these materials into their own undergraduate, graduate, or professional settings.
Organizers:
Catherine Manly, Fairleigh Dickinson University
Ajayi Anwansedo, Texas State University
Megan Atha, Florida Gulf Coast University
Ela Castellanos-Reyes, North Carolina State University
Duncan Culbreth, North Carolina State University
Hye Rin Lee, University of Georgia
Matthew Moreno, University of Central Florida
Doreen Mushi, Open University of Tanzania
Ceren Ocak, Georgia Southern University
Erin Ottmar, Worcester Polytechnic Institute
Son Pham, Nha Viet Institute
Yang Shi, Utah State University
Tiffany Wright, Pepperdine University
Workshop Website: https://sites.google.com/view/lak26alac/home
For more information: https://www.solaresearch.org/events/lak/lak26/leadership-academy/
Tuesday, April 28, 2026 - Full Day | 9:00 AM to 5:00 PM | In-Person
Type: Interactive Workshop Session
Description:
This is the proposal for the fourth interactive workshop on Measuring and Facilitating self-regulated learning (SRL). Measuring SRL using unobtrusive trace data and facilitating SRL through real-time analysis of such data have been identified as highly valuable research directions with significant implications especially for the LAK community. However, significant challenges remain in this area, including: (i) the detection, measurement, and validation of SRL processes using trace data is still a debated issue; (ii) the design principles for effective interventions and the complex conditions under which these interventions facilitate learning are not yet well understood; and (iii) the potential benefits of advanced AI techniques, such as ChatGPT, for learners, as well as the mechanisms through which learners can effectively co-regulate with AI, remain unclear. Our full-day workshop will address these challenges by offering participants hands-on experience with our AI-powered SRL tools and platforms, sharing tasks, data, and project outcomes. Additionally, we will initiate discussions about a joint annual international study to foster collaboration and advance SRL research. The workshop will feature an open call for contributions, roundtable discussions, and hands-on co-design activities. Expected outcomes include establishing a community of practice, forming collaborative projects, and developing follow-up joint publications.
Organizers:
Xinyu Li, Monash University
Tongguang Li, Monash University
Linxuan Zhao, Monash University
Jiaqi Xu,
Joni Lämsä, University of Oulu
Sanna Järvelä, University of Oulu
Susanne de Mooij, Radboud University
Inge Molenaar, Radboud University
Roger Azevedo, University of Central Florida
Megan Wiedbusch, University of Central Florida
Mladen Rakovic, Monash University
Yizhou Fan, Peking University
Dragan Gasevic, Monash University
Workshop Website: https://sites.google.com/monash.edu/lak26trace-srl
For more information: https://www.solaresearch.org/events/lak/lak26/leadership-academy/
For more information about the Doctoral Consortium, view their webpage here: https://www.solaresearch.org/events/lak/lak26/doctoral-consortium/
Tuesday, April 27, 2026 - AM Half Day | 9:00 AM to 12:30 PM | In-Person
Type: Interactive Workshop Session
Description:
This interactive workshop aims to support those who need to make policy (construed broadly) about the use of artificial intelligence and learning analytics in educational settings.. It will present examples of current policy making problems and use an identified current challenge to explore various approaches that can be used to broad and productive support stakeholder engagement into the policy making process.
Organizers:
Kirsty Kitto, University of Bergen
Simon Knight, University of Technology Sydney
Kalervo Gulson, University of Sydney
Christian M. Stracke, University of Bonn
Barbara Wasson, University of Bergen
Workshop Website: https://democratising-ai.github.io/posts/lak26-democratising-policy-in-the-age-of-ai/
Type: Interactive Workshop Session
Description:
Learning analytics research is often constrained by limited access to authentic educational data due to ethical, legal, and privacy concerns. These barriers reduce opportunities for replication, hinder innovation, and restrict cross-institutional collaboration. Synthetic data has emerged as a promising approach to address these challenges by generating artificial datasets that preserve the statistical properties and utility of real data while protecting individual privacy. The recent rise of Generative AI (GenAI), including large language models (LLMs), diffusion models, and generative adversarial networks (GANs), has significantly advanced the quality and scalability of synthetic data generation. Beyond structured tabular data, GenAI enables the creation of synthetic educational texts (e.g., assignments, peer feedback, dialogues) and synthetic images (e.g., learning materials, visualizations, simulated classroom scenes). These modalities create new opportunities for simulating learning scenarios, training predictive models, and conducting experiments otherwise limited by access restrictions. At the same time, they raise pressing questions around bias, fairness, and transparency in design and use.
This half-day workshop explores synthetic data in learning analytics, focusing on GenAI opportunities and challenges. Through hands-on activities and collaborative discussions, participants will gain practical skills, examine use cases, and co-develop a roadmap for ethical and innovative data use in education.
Organizers:
Farhad Vadiee, University of Bergen
Qinyi Li, University of Bergen
Oscar Deho, University of South Australia
Mohammad Khalil, University of Bergen
Srecko Joksimovic, University of South Australia
Workshop Website: https://lasd.slateresearch.ai/
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Generative Artificial Intelligence (AI) is reshaping Multimodal Learning Analytics (MmLA) by enabling richer insights into the semantics of learning interactions. Recent advances in Large Language Models (LLMs) and multimodal generative architectures open new possibilities for understanding meaning, intention, and context across modalities. This half-day symposium will bring together researchers, practitioners, and developers to explore
how LLMs and Generative AI can unlock semantic features in MmLA. The program includes flash presentations, guided discussions, and collaborative planning. Expected outcomes include a community manifesto, an independent publication of proceedings, and a proposal for a special section in the Journal of Learning Analytics. This workshop invites the community to envision the next generation of multimodal analytics tools and methods that
extend beyond behavior tracking to provide semantic-driven insights.
Organizers:
Xavier Ochoa – New York University
Daniele Di Mitri – German University of Digital Science
Andrew Zamecnik – University of South Australia
Daniel Spikol -University of Copenhagen
Namrata Srivastava – Vanderbilt University
Ruth Cobos – Universidad Autónoma de Madrid
Workshop Website: https://crossmmla.org/
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Artificial intelligence (AI) literacy has rapidly emerged as a critical competency, yet its integration into learning analytics (LA) remains underexplored. This workshop is designed as a catalyst for dialogue and collaboration, bringing together researchers, educators, and developers to examine how AI literacy can be defined, measured, and cultivated through LA approaches. By showcasing frameworks, assessment tools and pedagogical strategies, and engaging participants in collaborative design activities, the workshop will create a space to align emerging AI literacy research with LA methods and practices. Its goal is to seed working groups and collaborations that produce shared instruments, open resources, and actionable frameworks connecting AI literacy with learning analytics.
Organizers:
Yueqiao Jin (Monash University)
Yizhou Fan (Peking University)
Yuan Shen (Zhejiang University of Technology)
Lixiang Yan (Tsinghua University)
Mohammad Khalil (University of Bergen)
Thomas K. F. Chiu (The Chinese University of Hong Kong)
Davy Tsz Kit Ng (Education University of Hong Kong)
Mutlu Cukurova (University College London)
Workshop Website: https://sites.google.com/monash.edu/ai-lit-lak/home
Type: Interactive Workshop Session
Description:
The accelerating impact of artificial intelligence on the global labor market has intensified the demand for 21st-century competencies such as critical thinking, creativity, collaboration, and problem-solving. Postsecondary education institutions play a critical role in preparing students for this evolving landscape, yet face persistent challenges in defining, teaching, and assessing these competencies at scale. Curriculum analytics offers a data-driven approach to analyzing how competencies are integrated and evaluated, but significant challenges remain. These include concerns regarding the validity and reliability of data-driven methods for measuring competencies at scale, as well as limited empirical evidence on effective educational practices and the practical utility of the analytical pipelines. This workshop aims to address these open challenges by bringing together researchers, practitioners, and educators to exchange perspectives, share case studies, and discuss innovative approaches. Guest speakers will share novel analytical approaches (e.g., automated skill mapping, assessment annotation, competency modeling), current practices, and policies for adopting curricular analytics. Group discussions will follow to co-create principles and frameworks for advancing curriculum analytics in the context of 21st-century competencies. Expected outcomes include creating a white paper on establishing a community of practice and identifying priority areas for research and practice.
Organizers:
Zhen Xu, Columbia University
Vimukthini Jayalath, University of South Australia
Renzhe Yu, Columbia University
Abhinava Barthakur, University of South Australia
Shane Dawson, University of South Australia
Vitomir Kovanović, University of South Australia
Workshop Website: https://sites.google.com/view/lak26-caworkshop/home
Type: Interactive Workshop (Accepting Submissions)
Description:
While learning analytics has advanced our ability to capture complex learning
processes, methodological advancement is key in the field. This workshop will introduce a new learning analytics tool — HINA (Heterogenous Interaction Network Analysis). HINA offers a flexible technical solution to model and analyze interactions across diverse entities (e.g., students, behaviors, artefacts) as networked relationships. By transforming multimodal learning data into heterogeneous interaction networks (HINs), HINA enables researchers to uncover hidden patterns, test theoretical assumptions, and generate actionable insights about how learning unfolds. This half-day workshop consists of two parts: 1) a hands-on tutorial introducing HINA python package and web tool, empowering participants to apply network analytic techniques to their own learning datasets. 2) an interactive session, showcasing empirical applications through case studies from collaborators, demonstrating HINA’s utility across contexts (e.g., collaborative problem solving, self-regulated learning, human-AI interactions), as well as collaborative discussion about future directions for learning analytic tools’ development and adoption. This workshop will significantly benefit the learning analytics community by disseminating an innovative open-source tool for analyzing complex learning interactions, building capacity for network analysis methods through hands-on training, as well as fostering collaborations to advance methodological innovation.
Organizers:
Shihui Feng (University of Hong Kong, Hong Kong)
Baiyue He (University of Hong Kong, Hong Kong)
Mutlu Cukurova (University College London, UK)
Dragan Gasevic (Monash University, Australia)
Workshop Website: https://sites.google.com/view/hinaworkshoplak26/
Type: Interactive Workshop Session
Description:
In this half-day workshop, we will connect theories of agency with practitioner analytics, in order to support their practice in an increasingly datafied world. Agency is considered a core concept for teachers’ practice across social, educational, and psychology perspectives. Yet, analytics are often not designed specifically with agency in mind. Through presentations and interactive activities, we will synthesize methods, considerations, and obstacles for supporting teacher agency through learning analytics.
Organizers: Angela Stewart, Clara Belitz, Stephen Hutt and Caitlin Mills
Workshop Website: https://compubold1.my.canva.site/lak-agency-by-design
Tuesday, April 28, 2026 - PM Half Day | 1:30 PM to 5:00 PM | In-Person
Type: Mini-Track Symposium (Accepting Submissions)
Description:
This half-day workshop brings together interdisciplinary experts to examine how advances in AI can support wellness as a foundational part of the learning experience. Given the impact of wellness, wellbeing, and mental health on learning, the workshop will explore the ways in which learning analytics methods are poised to support a more holistic perspective that does not compartmentalize learning from the rest of our lives. We bring together experts with theoretical and methodological expertise from across the LA community (and beyond) to better understand how we can support learners’ wellbeing, while giving equal attention to the practical and safety implications that are critical to the success of these approaches.
Organizers:
Caitlin Mills, Tanya Gamby, Laura Allen, Srecko Joksimovic, Bec Marrone, Cati Poulos, Walter Reilly, David Kil, George Siemens
Workshop Website: https://lak26wellness.netlify.app/
Type: Interactive Workshop Session
Description:
Learning analytics feedback has matured significantly. Now, the emergence of generative AI and other advanced technologies presents unprecedented opportunities to augment personalised feedback at scale. However, this technological evolution raises critical considerations that demand systematic examination: measuring feedback impact, understanding student feedback engagement patterns, and reconceptualising feedback literacy for both students and teachers in an AI-enhanced landscape. This half-day workshop, fourth in the series of Personalised Feedback at Scale workshops at LAK, adopts an interactive approach combining focused small group discussions with targeted presentations to address these pivotal themes. The session culminates in a synthesis plenary that consolidates participants’ insights and identifies emerging research directions. The workshop's key contribution is fostering greater appreciation of fundamental feedback concepts and theoretical underpinnings, ensuring LA feedback tools remain well-grounded in established feedback theory even as the field advances technologically. Participants will gain new conceptual frameworks for understanding LA and AI-enhanced feedback, identify promising research directions, and establish collaborative connections with peers addressing similar challenges. Workshop insights will be synthesised and shared at the main LAK conference poster session, ensuring broader community dissemination.
Organizers:
Lisa-Angelique Lim, University of Technology Sydney
Ioana Jivet, FernUniversität in Hagen
Rafael Ferreira Mello, UFRPE
Yi-Shan Tsai, Monash University
Joshua Weidlich, University of Zurich
Workshop Website: https://sites.google.com/uts.edu.au/lak26-personalisingfeedback/welcome
Type: Mini-Track Symposium (Accepting Submissions)
Description:
This workshop will explore how Learning Analytics (LA) can advance human-centered Generative AI-enhanced Smart Learning Environments (SLEs) in hybrid learning contexts. We will examine how LA supports personalization, adaptive feedback, orchestration, and data-driven decision-making while ensuring transparency, fairness, and human agency. Through paper presentations and collaborative discussions, participants will identify opportunities, challenges, and ethical considerations. The workshop aims to foster interdisciplinary exchange and co-develop a research agenda for the advancement of human-centered Generative AI-enhanced SLEs through the use of LA.
Organizers:
Pedro Manuel Moreno-Marcos, Universidad Carlos III de Madrid, Spain
Miguel L. Bote-Lorenzo, Universidad de Valladolid, Spain
Patricia Santos, Universitat Pompeu Fabra, Spain
Peter Brusilovsky, University of Pittsburgh, USA
Mutlu Cukurova, University College London, United Kingdom
Dragan Gašević, Monash University, Australia
Nancy Law, University of Hong Kong, Hong Kong
Marcus Specht, FernUniversität, Germany
Daniel Spikol, University of Copenhagen, Denmark
Katrien Verbert, Katholieke Universiteit Leuven, Belgium
Carlos Delgado Kloos, Universidad Carlos III de Madrid, Spain
Juan I. Asensio-Pérez, Universidad de Valladolid, Spain
Davinia Hernández-Leo, Universitat Pompeu Fabra, Spain
Workshop Website: https://sites.google.com/view/la4genaisle
Type: Interactive Workshop Session
Description:
Following the high engagement and critical dialogue at AIED 2025, this workshop advances the conversation on Human-AI teaming as a site of synergy in learning analytics. As artificial intelligence (AI) systems increasingly move beyond functioning as passive tools to becoming active teammates in collaborative learning environments, there is an urgent need to examine their implications for inclusion, equity, and team dynamics. Drawing on the Machines as Teammates (MaT) framework, this workshop will address key questions for the learning analytics community: How should AI be conceptualized and evaluated as a teammate in collaborative settings? What analytic approaches are needed to understand and support equitable participation when humans and AI collaborate? Which design principles can ensure AI teammates enhance rather than diminish group processes, particularly in STEM contexts where representation gaps persist? The workshop will combine expert presentations, a panel discussion, and interactive breakout sessions to develop research and design agendas around Human-AI teaming. It will also highlight ongoing work on Generative AI-driven interventions and Belonging-Centered Interactions, with a particular focus on their application in real-world educational contexts. The session advances a learning analytics perspective on how Human-AI teaming can shape the future of inclusive, data-informed learning environments.
Organizers:
Nia Nixon, University of California, Irvine
Srecko Joksimovic, University of South Australia
Andrew Zamecnik, University of South Australia
Jaeyoon Choi, University of California, Irvine
Seehee Park, University of California, Irvine
Mohammad Amin Samadi, University of California, Irvine
Workshop Website: https://sites.google.com/view/lak-workshop/home
Type: Mini-Track Symposium (Accepting Submissions)
Description:
As the adoption of digital learning materials in modern education systems is increasing, the analysis of reading behavior and their effect on student performance gains attention. The main motivation of this workshop is to foster research into the analysis of students’ interaction with digital textbooks, and find new ways in which it can be used to inform and provide meaningful feedback to stakeholders: teachers, students and researchers. The previous years workshops at LAK19 and LAK20 focused on reading behavior in higher education, and LAK21 to LAK25 on secondary school reading behavior and pre/post COVID-19 pandemic changes. Participants of this year’s workshop will be given the opportunity to analyze several different datasets, including secondary school prediction of academic performance for more than one subject. As with previous years, additional information on lecture schedules and syllabus will also enable the analysis of learning context for further insights into the preview, in-class, and review reading strategies that learners employ. In addition, this workshop will accept a wide range of research topics on learning analytics, educational technology, and learning support systems, including applications of GenAI in reading systems, proposals for new educational systems, new evaluation methods, and so on.
Organizers:
Brendan Flanagan, Kyoto University
Atsushi Shimada, Kyushu University
Owen Lu, National Chengchi University
Albert C.M. Yang, National Chung Hsing University
Fumiya Okubo, Kyushu University
Hsiao-Ting Tseng, National Central University
Hiroaki Ogata, Kyoto University
Workshop Website: https://sites.google.com/view/lak26datachallenge/home
Type: Mini-Track Symposium (Accepting Submissions)
Description:
Teachers today navigate a complex landscape where Learning Analytics and Generative Artificial Intelligence promise to support evidence-based decision-making. The challenge lies in ensuring these technologies empower rather than diminish teacher agency. This workshop explores human-in-the-loop approaches that integrate data-driven insights with GenAI-assisted workflows to enhance instructional design, student engagement, assessment, and differentiated strategies. It will convene researchers, practitioners, and policymakers to critically examine both opportunities and risks of LA. Emphasis will be placed on socio-technical perspectives that consider not only technical advantages but also trust, ethics, transparency, and cultural context. Organized as a mini-track symposium, the workshop will feature accepted submissions such as short papers, position papers, and work-in-progress reports, alongside interactive activities such as design sprints and group discussions. Outcomes will include a community-authored synthesis report and an open repository of contributions.
Organizers:
Shashi Kant Shankar, Ahmedabad University, India
Ramkumar Rajendran, Indian Institute of Technology, Bombay, India
Rwitajit Majumdar, Kumamoto University, Japan
Shitanshu Mishra, MGIEP, UNESCO, India
Ashwin T. S., Vanderbilt University, USA
Kshitij Sharma, NTNU, Norway
Workshop Website: https://sites.google.com/ahduni.edu.in/lak2026/home