Panels

Festival of Learning 2026 will host a series of panel discussions that bring together researchers, practitioners, and industry leaders to explore timely questions at the intersection of AI, learning, and educational practice.

Additional panels and details will be announced soon.

Rethinking Assessment in the Age of AI: From Snapshots to Continuous Evidence Infrastructure

Time: Monday, June 29, 3:45–5 PM

Moderator: Danielle McNamara (Arizona State University)

Panelists:

Ken Koedinger (Carnegie Mellon University)
Debshila Basu Mallick (OpenStax – Rice University)
Mutlu Cukurova (University College London)
Rene Kizilcec (Cornell University)

Abstract: Advances in AI systems—including agentic workflows, persistent memory, and context-aware models—are transforming learning into a more continuous, adaptive, and multimodal process that unfolds across dialogue, simulation, collaboration, and real-world application over time. At the same time, higher education is moving toward more authentic, skill-based, and portfolio-driven approaches to assessment. Yet most assessment systems remain episodic and disconnected from how learning actually occurs, capturing isolated snapshots rather than learner development over time. This panel brings together researchers and practitioners to explore how assessment must evolve in response. Topics include process-based and multimodal assessment, longitudinal learner records, AI-supported feedback and evaluation, and emerging forms of AI-native assessment designed for continuous, context-aware learning environments. Panelists will also discuss the infrastructure required to support these approaches, including interoperability, learner-centered evidence systems, memory architectures, and embedded evaluation frameworks that allow evidence of learning to accumulate, persist, and remain interpretable across contexts over time. By connecting perspectives from AI, learning analytics, assessment, and educational practice, the session aims to advance a more integrated vision of assessment—one that reflects how learning actually happens and supports richer, more meaningful representations of learner growth.

Learning Research Communities in an AI-Saturated World (Are We All AIED Researchers Now?)

Time: Friday, July 3, 10:45–12 AM

Moderator: Alyssa Wise (Vanderbilt University)

Panelists:

Olga Viberg (KTH Royal Institute of Technology)
Blazenka Divjak (University of Zagreb)
Heisawn Jeong (Hallym University)

Abstract: AI has quickly become a common point of attention across learning and learning technology research. Schools want AI guidance, funders want AI proposals, universities want AI strategies, and commercial platforms want access to educational markets, shaping practices at scale often faster than research communities can design for and study them. Within research itself, AI may support data collection, analysis, writing, and design, but it also raises concerns about quality, quantity, authorship, trust, and how we maintain the time, focus, and shared attention needed to build knowledge collectively. This panel asks what happens to our learning communities when AI becomes a shared object of inquiry, professional tool, and practical concern. This does not mean that all learning research is, or should become, AI research; rather, AI is currently a field-level presence with broad-reaching ripples, even for scholars whose central questions lie elsewhere. Learning analytics, learning sciences, and European technology-enhanced learning have each developed through distinct histories, questions, methods, theories, and design commitments. As AI brings work from these traditions into increasing contact, what differences remain intellectually important? Which boundaries may no longer be useful? Where do we need more collective action in service of real-world impact? Discussion will explore questions about AI’s role in education and what counts as a valued outcome now; how AI might help us do better, not just faster, research; what aspects of teaching and learning should be augmented, automated, reimagined, or protected; and how human agency is conceptualized in AI-mediated settings. The goal is not for every community to become AI-focused, but to interrogate what AI’s rise means for the questions, methods, values, and responsibilities of each community, both individually and collectively.