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

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.