CSCL Symposium | cY_THEORY-TOOLBOX | Towards a Robust Theory Toolbox for CSCL: Mechanisms, Models, and AI

This symposium aims to foster new scientific discussions about how the CSCL (Computer-Supported Collaborative Learning) field can advance towards more robust theories. Such advancements depend on further development and formalization of models, causal mechanisms, and the creative application of different types of generative AI. The problem description is rooted in the increasing gap between theoretical positions and actual data, also emphasizing that data alone cannot explain any phenomenon. The overarching argument is that computational modeling approaches can be employed to formalize models of collaborative scripts, temporal collaborative processes, multi-level collaboration, and multimodal activities and that varied uses of AI can amplify such efforts. These models can be based on causal mechanisms and can be generalized to collaborative learning phenomena rather than just specific populations. We provide a rich view of the CSCL field that includes pathways to theoretical robustness, practical tools and solutions for collborative learning in the age of AI.