Mining Collective Intelligence and Predicting Disruptive Paradigm Shifts via Human‐Aware AI

Scientific progress hinges on the interplay between collective intelligence and transformative paradigm shifts, yet predicting these revolutionary events remains a persistent challenge. This study introduces a novel human‐aware AI framework that integrates the evolution of knowledge structures with the social dynamics of scientific communities to forecast groundbreaking innovations. Leveraging graph convolutional neural networks (GCNNs), we construct a hybrid higher‐order network that unifies a domain knowledge graph—derived from millions of scientific publications—with a scientist collaboration‐competition space, capturing both cooperative and competitive interactions among researchers. This approach quantifies collective intelligence by generating embeddings that reflect the intricate relationships between knowledge content and human agency. By analyzing thematic knowledge distances and social proximities within this integrated network, we identify pairs of scientific domains poised for disruptive convergence. Dynamic analysis of these embeddings further enables temporally precise predictions of paradigm shifts. Applied to the life sciences, our framework successfully aligns with historical milestones, such as Nobel Prize‐winning discoveries, demonstrating its predictive power. This work offers a scalable, interpretable tool for anticipating scientific revolutions, bridging the gap between knowledge evolution and social dynamics, and providing actionable insights for fostering innovation across disciplines.