Complex System Perspectives on Learning in Networked Digital Communities

In the rapidly evolving digital landscape, learning is increasingly understood as a complex adaptive system shaped by nonlinear interactions among cognitive, social and technological agents. Despite the fact that data‐driven and AI‐enhanced approaches have led to an increase in the capacity to model this complexity, there remains a paucity of theoretical integration with the learning sciences, resulting in a fragmentation of interpretations of learning dynamics. The present study proposes a networked complex learning model that unites complex systems theory, the connectivity paradigm and digital learning ecologies to advance a systemic understanding of learning. The present study employs a bibliometric analysis of publications indexed in the Web of Science (2000–2025) to apply co‐word, co‐citation and thematic mapping techniques within the Bibliometrix (R‐Biblioshiny) framework, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines for transparency and methodological rigour. The results indicate an exponential increase in research activity after 2018, which is indicative of theoretical expansion towards systemic, emergent and connective models of learning. Thematic mapping reveals global clustering around AI‐mediated and network‐based approaches, while ethical and conceptual integration remains underdeveloped. The study concludes that combining complex systems theory with data‐intensive modelling offers a holistic, adaptive and ethically responsive framework for understanding learning as a dynamic behavioural system in digital environments.