From Provocations to Possibilities: Knowledge Building and the Future of CSCL with Generative AI

Abstract This paper examines Knowledge Building—past, present, and future—reflecting on its progress and situating its contributions within CSCL’s visions and tensions to inform next-generation Knowledge Building and CSCL. Knowledge Building aligns with many CSCL approaches for collaborative knowledge construction while foregrounding collaboration as knowledge creation—an agenda much needed in the generative AI era for expanding CSCL’s scope, identity, ecological and real-world impact. We first examine the foundations and goals of Knowledge Building, aligning theory, design, and technology for knowledge creation. We then review state-of-the-art Knowledge Building research and consider how it addresses CSCL visions and provocations, offering possibilities around scripting versus learner agency, collaborative groups versus communities, integrated design-based approaches, researcher-focused versus student-driven analytics, and bounded contexts versus open communities. Moving forward, we discuss how Knowledge Building can inform AI designs in CSCL and how generative AI can enrich Knowledge Building. Continuing with a knowledge-creation focus, we propose that AI designs should support transformation and idea improvement, advance community and public knowledge, balance guidance with epistemic agency, and develop technology as infrastructure for growing student epistemic capacity. We conclude by proposing future theoretical, methodological, and design directions, including broadening domains and contexts, engaging diverse learners and open communities, and studying collaboration at different scales for educational impact. Knowledge Building offers a future-oriented perspective that provokes new questions about collaboration, knowledge creation, and human–AI partnership in CSCL.