Companion Proceedings of the 16th International Learning Analytics and Knowledge Conference (LAK’26)

We are very pleased to welcome you to the Sixteenth International Conference on Learning Analytics and Knowledge (LAK26), organized by the Society for Learning Analytics Research (SoLAR). This year’s conference is taking place in Bergen, Norway, between April 27th and May 1st, 2026.

The theme for the 16th annual LAK conference is Learning Analytics and AI Synergy. The increasing integration of Artificial Intelligence (AI) into diverse learning contexts necessitates carefully examining its impact and potential to enhance learning. Learning Analytics (LA), as a human-centered and multidisciplinary field, offers significant potential for theory-driven, process-oriented, and multimodal investigations of AI integration and impact on learning and teaching. This year’s conference focuses on the synergistic relationship between AI and LA, exploring how AI technologies can be strategically used to empower all phases of the LA cycle, from data collection and analysis to interpretation and communication, through stakeholder-informed actions. The conference also seeks to highlight how LA can be strategically employed to augment the research and practice of AI in education. Accordingly, we invited research that investigated how the synergy of LA and AI could contribute to more nuanced understandings of learning and teaching, facilitate data-informed decision-making, and promote evidence-based educational improvements. Furthermore, we aim to foster a critical dialogue on the responsible deployment of AI, ensuring that its application complements and reinforces the fundamental principles of LA. Therefore, the conference invited contributions that rigorously explored the theoretical, methodological, analytical, and ethical considerations involved in harnessing the combined potential of LA and AI for advancing learning-related research and practice.

Two excellent keynote talks and a keynote panel present compelling examples of expanding the horizons of learning analytics through synergy with AI, but also raise important questions regarding the effects of such an expansion on the learning analytics field itself. Wilfried Admiraal is a social psychologist and full professor at the Centre for the Study of Professions of Oslo Metropolitan University in Norway. Wilfried’s keynote focuses on the critical challenge of redesigning pedagogies to make meaningful and effective use of the current, often AI-based, digital tools and particularly the role of learning analytics in informing  the redesign of pedagogies, with a focus on students’ cognitive, behavioural, emotional and social engagement with learning. Q. Vera Liao is an Associate Professor of Computer Science and Engineering at the University of Michigan, with significant experience in industrial research done at Microsoft Research and IBM T.J. Watson Research Center. In her keynote, Vera focuses on the distinct kinds of risks that AI poses to human intelligence, primarily threats to the integrity of people’s information-seeking and learning processes, as well as strategies for overcoming such challenges. The last day of the conference starts with an interactive keynote panel, titled “Impact of the Revised Learning Analytics Definition”, the panel reflects on the development and implementation of the revised definition of learning analytics over the past year, and explores the implications of the reimagined definition for the learning analytics community and its future research directions. The conference also features two additional panels. One of them convenes leading LA researchers to critically examine the value of LA for researching generative AI in education and also to discuss AI as a set of tools that can amplify, but also distort, LA processes if not carefully designed, developed, and deployed. The other panel, organised by the local host, engages the panelists in topics revolving around the central conference theme, with a specific focus on the local experiences, regulatory requirements, and cultural and other factors that shape the interplay of LA and AI in Norway and Nordic region.

This year’s conference theme encouraged researchers and practitioners to consider distinct ways that LA and AI interact, contribute to and complement one another, including but not limited to understanding learning and teaching with AI; AI-supported educational decision-making; the use of AI to empower LA process; scaling LA interventions through AI; expanding the theoretical perspectives and methodological approaches to to advance the synergy between LA and AI; and ensuring ethical and responsible AI use in education. The breadth and currency of the topical scope might partially explain a very large number of high-quality submissions we have received this year, breaking all previous records, and we are extremely grateful for all those who decided to submit the results of their latest research efforts to LAK26. The research track had 372 submissions (254 full paper submissions and 118 short paper submissions). This represents an increase of 9.4% in the total number of submissions compared to last year. These papers came from research institutions of 46 countries (19 in Europe, 14 in Asia, 6 in South America, 2 in North America, 3 in Africa, and 2 in Australia and Oceania). Maintaining the high quality of the conference, the program committee for the research track consisted of 344 researchers from the field of learning analytics, educational data mining, learning sciences, educational technology, and related disciplines. Of these, 84 were senior members, all recognized leaders in the field and highly involved in service to the learning analytics community. Overall, from the 372 research submissions, the program committee worked very hard to select 107 papers (67 full research papers and 40 short research papers) that are included in the proceedings of the 16th Learning Analytics and Knowledge Conference. The acceptance rate for both full and short research tracks is 29%.

The rigorous selection process for LAK includes an initial phase of review of at least two program committee members. Authors are then given a short time to provide an optional rebuttal to the remarks and comments raised in the initial review in which they can answer specific questions raised by reviewers (if any) or flag any inaccuracies, omissions, or errors in the reviews. This is followed by the meta-review phase during which, for each submission, a senior program committee member, having carefully reviewed the initial reviews and the authors’ rebuttal (if submitted), provides a summary meta-review and final recommendation to the program chairs. We are most grateful for all the hard work by the program committee and their insightful and constructive comments and reviews. These proceedings could not have been possible without their generous help and support.

We would also like to emphasize our ongoing gratitude for the efforts made by all involved in the learning analytics community. We very much understand the complexity of work and life pressures impacting on our time commitments, and priorities. The high level of support and commitment shown by our colleagues to ensure that the presented and published papers have received high quality reviews and feedback is highly valued and appreciated. We want to thank you for the important efforts you have devoted that have allowed this conference to continue as a premier scientific event fostering the scholarly exchange of ideas of the highest caliber.

We hope that LAK26 participants and other readers of these proceedings will find value in the broad range of contributions to the field of learning analytics contained within. The rapid development and adoption of AI-based technologies, especially generative AI, as well as technological developments more broadly are opening many new opportunities for learning analytics research and practice, but also introducing novel challenges that call for novel methodological approaches and theoretical frameworks. Likewise, further work and novel approaches are needed to assure responsible use and analytics of learning-related data, meet the needs and expectations of diverse stakeholders, as well as ensure ethical conduct in learning analytics research and practice and fair and just treatment of all learners. We hope that the scholarly exchanges at this conference, including the paper presentations, keynotes, panels, and both formal and informal discussions among the participants will contribute to addressing the aforementioned and related challenges and bring us closer to the ultimate objective of understanding and advancing learning and the environments in which it occurs. 

LAK26 Companion Proceedings