Knowledge Co‐Creation by Human–Machine Social Interaction: Based on Decision Field Theory

ABSTRACT

Generative AI is reshaping the paradigm of knowledge creation, shifting it from a single human‐centred mode to a human–machine co‐creative process. However, cognitive differences, fragmented interactions and lack of contextual resonance between humans and machines make traditional human‐centred knowledge creation theory, as exemplified by the SECI model, insufficient for explaining this process. Drawing on decision field theory and using the SECI model as a process framework, this study develops a dynamic model of human–machine knowledge co‐creation. Simulation results show that the effective unfolding of co‐creation depends on sustained interaction and timely feedback. In routine tasks, the formation of shared judgement structures is influenced more by computational capacity, whereas in creative tasks it depends more on knowledge stock. This study responds to the boundary limitations of traditional knowledge creation theory, incorporates machine actors into knowledge creation analysis, and provides a mechanism‐level basis for augmented intelligence in human–machine co‐creation.