Keynote speakers 2026

Dr. Bart Rienties is Professor of Learning Analytics and Director of the Institute of Educational Technology (IET) at the Open University UK (OU). Since IET was established in 1970 we have been central to the OU’s mission to be the world leader in researching, innovating and delivering supported, open and distance learning. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Learning Design, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects, currently leads the £5M Research Capacity Hub for ESRC, and has received a range of awards for his educational innovation projects. Until May 2026 he is President of the Society of Learning Analytics Research (SoLAR), the largest researcher community on learning analytics. He has published over 300 academic outputs, and is the 1st most published and cited author on learning design and learning analytics in the period 2014-2023 (Drugova et al. 2024), the 2nd most published author on learning analytics in period 2004-2024 (Kılıç & İzmirli, 2024), Networks in Education in period 1969-2020 (Saqr et al. 2022), and social network analysis in period 1988-2022 (Gandasari et al., 2024)

The rapid emergence of generative AI has prompted fundamental questions about the future of education, assessment, learner support, and the role of educational research (Giannakos et al., 2025; Rienties et al., 2026). If AI systems can now generate feedback, answer questions, summarise content, and support students in real time, is there still a need for learning analytics? In this keynote, I argue that the answer is not only yes, but that learning analytics is more important than ever.

Drawing on more than a decade of large-scale learning analytics research and innovation at The Open University UK (Herodotou et al., 2019; Holmes et al., 2019; Nguyen et al., 2020; Rienties, 2021), this keynote will explore what we have learned from deploying data-informed approaches in open and distance education. I will reflect on institutional experiences with OU Analyse, learning design analytics, and emerging work on AI-supported learning, including AIDA and related projects. These examples show that effective educational AI cannot be built on technical capability alone. It requires robust evidence about learning, careful attention to pedagogy, ethical governance, and a deep understanding of the diverse contexts in which students and teachers operate (Rienties et al., 2026).

The keynote will consider how learning analytics can help move the educational AI debate beyond hype, fear, and narrow questions of academic misconduct. Instead, learning analytics can provide the empirical, theoretical, and ethical foundations needed to design AI systems that genuinely support learning, enhance agency, and strengthen trust. In particular, I will discuss how analytics can help us understand when, why, and for whom AI-supported interventions work; how educators can remain meaningfully in the loop; and how institutions can balance innovation with responsibility, transparency, and care.

Ultimately, the keynote will invite participants to reimagine learning analytics not as a technology of prediction or surveillance, but as a human-centred, evidence-informed practice for shaping more inclusive, supportive, and intelligent educational futures in the age of AI.

Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44, 2518-2544. https://doi.org/10.1080/0144929X.2024.2394886

Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50(6), 3064-3079. https://doi.org/10.1111/bjet.12853

Holmes, W., Nguyen, Q., Zhang, J., Mavrikis, M., & Rienties, B. (2019). Learning analytics for learning design in online distance learning. Distance Education, 40(3), 309-329. https://doi.org/10.1080/01587919.2019.1637716

Nguyen, Q., Rienties, B., & Richardson, J. T. E. (2020). Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting. Assessment and Evaluation in Higher Education 45(4), 594-606. https://doi.org/10.1080/02602938.2019.1679088

Rienties, B. (2021). Implementing Learning Analytics at Scale in an Online World: Lessons Learned from the Open University UK. In J. Liebovitz (Ed.), Online Learning Analytics (pp. 57-77). Auerbach Publications.

Rienties, B., Coughlan, T., Domingue, J., & Herodotou, C. (2026). New systems of learning for distance learning institutions? A six-study review of implementing AIDA. Computers and Education: Artificial Intelligence, 10, 100607. https://doi.org/https://doi.org/10.1016/j.caeai.2026.100607

Dr. Eduardo Oliveira is a Senior Lecturer and Assistant Dean (Diversity and Inclusion) at the University of Melbourne. Drawing on 16 years of industry experience as a senior software engineer for corporations like Motorola and Samsung, his multidisciplinary research bridges Artificial Intelligence, Learning Analytics, and Software Engineering. Guided by models of self-regulated learning, his predominantly quantitative research combines learning analytics trace data with self-reported surveys to explore responsible human-AI interactions, writing analytics, and the design of scalable pedagogical infrastructures that assure learning and preserve student agency. Actively translating research into practice, he has deployed “human-in-the-loop” AI tools for over 4,700 students and a GenAI framework upskilling 1,400+ staff. He has secured over $1.7M AUD in competitive funding, leading major initiatives in AI in education for the Commonwealth, the Australian eSafety Commissioner, TOEFL® (USA), and CNPq (Brazil). A celebrated educator, his accolades include SFHEA (2025), the GEM Scott Teaching Fellowship (2024), the Edward Brown Award for T&L Excellence (2023), and the FEIT Kelvin Medal for T&L Excellence (2022).

As Generative AI reshapes higher education, institutions in Australia have faced a critical mandate from regulatory bodies like TEQSA: move beyond the defensive policing of academic misconduct and proactively scaffold responsible AI use. This keynote presents an applied, practical approach to cultivating human-AI hybrid intelligence in the classroom while rigorously aligning with institutional secured assessment requirements. Drawing on large-scale curriculum innovations at the University of Melbourne that have impacted over 6,000 staff and students, Dr. Oliveira will walk attendees through a suite of successful learning analytics and AI initiatives he has led to assure learning. The presentation will highlight applied examples such as the deployment of the IntelliJourney platform to support self-regulated learning and a university-wide GenAI literacy framework developed through the GEM Scott Fellowship. It will also showcase the engineering of custom “human-in-the-loop” tools including GenAI code-reviewers that provide formative feedback rather than automated solutions, and motivational modelling platforms that help students refine software requirements while strictly preserving their cognitive agency. Furthermore, the session will examine the strategic transition of large-size classrooms toward Interactive Oral Assessments and other secured assessment approaches as robust mechanisms to authenticate student competence. Ultimately, this keynote offers educators and institutional leaders a scalable blueprint for leveraging learning analytics and AI to foster learner agency, capture reliable evidence of learning, and rebuild trust in an AI-augmented educational landscape.

The rapid expansion of artificial intelligence (AI), particularly generative AI (GenAI), is transforming education in unprecedented ways. This shift builds on a longer co-evolution between AI, the learning sciences, educational technology, and computer-supported collaborative learning (CSCL). From early intelligent tutoring systems to learning analytics and adaptive environments, AI has historically focused on supporting individual cognition, whereas recent developments increasingly intersect with the social and interactional dimensions central to CSCL. Contemporary AI can both augment learning by providing cognitive support and reshape tasks to promote deeper, collaborative knowledge construction. However, its integration also raises critical concerns: inappropriate use may lead to inversion effects, such as over-reliance that diminishes learners’ cognitive and collaborative engagement. Its effectiveness further depends on key moderators, including students’ AI literacy and teachers’ technological, pedagogical, and ethical competencies. From a CSCL perspective, a central issue is how AI reshapes collaboration itself and can function as an active socio-cognitive partner. Human–AI–human collaboration can be understood as a triadic system encompassing student–student, student–AI, and group–AI interactions. Overall, AI challenges core CSCL assumptions by redefining agency, participation, and the boundaries of collaboration. This keynote synthesizes conceptual, empirical, and design-oriented perspectives to examine how may shape future collaborative learning environments.

Teemu Roos is a Professor of Computer Science at the University of Helsinki. He is also the leader of the AI Education programme at the Finnish Center for AI. Roos is the lead instructor of the Elements of AI online course has a pivotal role in Finland’s unique, inclusive AI strategy, with over 450 000 participants to date.

The abrupt intrusion of AI into classrooms has left educators with a feeling of confusion and loss of control. Both teachers and students are using AI to varying degrees, with very little institutional support, leading to arbitrary results. What was once a skills gap has become an acute rupture that threatens to marginalize large groups of individuals. These challenges urgently call for better understanding of AI’s impact on existing pedagogical frameworks, and new research-based solutions that can lead to new approaches where AI is a part of the solution rather than a problem to fix. Building on this tension, this keynote will review preliminary research on teachers’ reactions to AI in the classroom, demonstrate new AI tools that are better aligned with existing education systems, and present new pedagogical approaches that support students’ data agency and transformative AI literacy. More than offering a ready and complete vision for the future, the talk will identify knowledge gaps and research opportunities to draw attention to several outstanding issues in AI and education.