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.
Transferring AIED Research into Action: From Publications to Educational Impact
Time: Tuesday, June 30, 2–3:15 PM
Abstract: AIED has produced decades of research demonstrating how AI can support teaching, lerning and other educational practices. Yet, translating research findings into large-scale educational impact remains a significant challenge. This panel brings together leaders from academia, industry and nonprofit organizations to discuss how AIED innovations can move beyond research prototypes and become sustainable solutions that improve educational practice. Panelists will share lessons learned from deploying AIED technologies highlighting both successes and persistent barriers. Through an interactive discussion, the panel will explore strategies for strengthening partnerships among researchers, educators, policymakers, and industry stakeholders to accelerate the responsible adoption of evidence-based AI solutions in education.
Creating Infrastructure that Strengthens the Quality and Impact of Educational Research
Time: Tuesday, June 30, 3:45–5 PM
Moderator: Jeremy Roschelle (Digital Promise)
Panelists:
- Hiroaki Ogata (Kyoto University)
- Kirk Vanacore (Cornell University)
- Tanja Kaeser (EPFL)
- Roberto Martinez-Maldonado (Monash University)
Abstract: As educational research teams strive for greater scale, broader partnerships, and more timely, actionable insights, robust infrastructure has become essential to today’s state-of-the-art projects and initiatives. In AI education, this encompasses benchmarks, datasets, and domain-specific models; in educational data mining and learning analytics, it includes secure data access, privacy frameworks, and cross-industry testing environments. Crucially, this infrastructure is socio-technical—requiring data governance, privacy protections, and security policies alongside technical capabilities. This interactive session brings together international panelists to offer their perspective on these systems in their country and region. Through panel discussion and audience Q&A, we will explore: Why is infrastructure critical now? What frameworks or advances are most urgently needed so that our research can be more impactful? How can researchers effectively collaborate with educators, policymakers, industry and internationally? Ultimately, if these efforts succeed, what impacts will a Festival of Learning celebrate five years from now that it cannot celebrate today?
Old AIED, New AI: What has changed? What still matters?
Time: Wednesday, July 1, 2–3:15 PM
Abstract: The rapid rise of generative AI and large language models has dramatically accelerated public interest in AIED, creating unprecedented opportunities and challenges for individuals, institutions, and society as a whole. Yet many of the core questions that have shaped the AIED field for decades remain highly relevant: How do people learn? How can AI support effective teaching and learning? How do we ensure educational technologies are trustworthy, equitable, and pedagogically sound? At the same time, the field faces an important question of its own: in a rapidly evolving AI landscape, which earlier AIED topics, techniques, and methods remain relevant, which require adaptation, and which have become less useful? This panel brings together senior and emerging experts in the field to examine what is genuinely new in the current wave of AI innovation, which foundational principles of AIED continue to matter, and how past approaches can inform present and future work. Through reflections on past successes, emerging technologies, and future directions, panelists will explore how the field can build on its rich history while responding to a rapidly evolving AI landscape—without reinventing the wheel or falling into the same traps of decades past.
AI as Peer Reviewers: Promise, Pitfalls, and Possibilities
Time: Thursday, July 2, 2–3:15 PM
Abstract: Researchers are increasingly using AI to support a wide range of academic activities, including literature reviews, manuscript writing, editing, and the assessment of their own work prior to submission. As these tools become more capable and widely adopted, a new question is emerging: Should AI also be used to review other people’s research? From identifying methodological weaknesses and missing citations to evaluating clarity, novelty, and scientific rigor, large language models are beginning to demonstrate capabilities that overlap with traditional peer-review tasks. At the same time, concerns remain about bias, accountability, confidentiality, copyright, and the potential impact of AI on the norms and values of scholarly communication. Will AI write and review our work? Where should we draw the line between acceptable and unacceptable uses of AI in the scholarly review process? This panel brings together editors, reviewers, researchers, and practitioners to explore the promise, pitfalls, and possibilities of AI-assisted peer review. Panelists will discuss current practices, emerging evidence, and whether AI should serve as a reviewer, a reviewer assistant, or a catalyst for reimagining the peer-review process itself.
Learning Research Communities in an AI-Saturated World (Are We All AIED Researchers Now?)
Time: Friday, July 3, 10:45 AM–12 PM
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.