  The current societal challenges exceed the capacity of human individual or
collective effort alone. As AI evolves, its role within human collectives is
poised to vary from an assistive tool to a participatory member. Humans and AI
possess complementary capabilities that, when synergized, can achieve a level
of collective intelligence that surpasses the collective capabilities of either
humans or AI in isolation. However, the interactions in human-AI systems are
inherently complex, involving intricate processes and interdependencies. This
review incorporates perspectives from network science to conceptualize a
multilayer representation of human-AI collective intelligence, comprising a
cognition layer, a physical layer, and an information layer. Within this
multilayer network, humans and AI agents exhibit varying characteristics;
humans differ in diversity from surface-level to deep-level attributes, while
AI agents range in degrees of functionality and anthropomorphism. The interplay
among these agents shapes the overall structure and dynamics of the system. We
explore how agents' diversity and interactions influence the system's
collective intelligence. Furthermore, we present an analysis of real-world
instances of AI-enhanced collective intelligence. We conclude by addressing the
potential challenges in AI-enhanced collective intelligence and offer
perspectives on future developments in this field.
