A Study on Theories and Methods of Distributed Cognition-Driven Intelligent Design for Crowdsourced Products
Yuming Guo · Business, management and economics · 2026
User demand-driven crowdsourced product innovation is inherently uncertain, characterized by a decision-making process where design cognition evolves from vague to precise and from abstract to concrete. To tackle the challenges posed by perceptual change propagation resulting from design uncertainty in intelligent information sharing during crowdsourced product design, this study proposes a theoretical framework and methodology centered around distributed cognition-driven intelligent design. To this end, a systematic framework is established from the perspectives of individual designers, concept development, and designer community evolution. The mechanisms of distributed cognition, informed by design experiences and activities within intelligent design environments, are thoroughly analyzed. Furthermore, the study offers theoretical models and solution approaches for addressing key challenges in collective intelligence innovation, serving as a valuable reference for the practical application of distributed cognition-driven intelligent design. This research aims to effectively construct a unified theoretical framework that bridges conceptual gaps between human–machine collaboration, cognitive modeling, and artificial intelligence (AI)-augmented design. It specifically addresses the cross-domain challenge of “change propagation under perceived design uncertainty,” thereby establishing a new interdisciplinary field.