众智是团队通过协作、沟通与知识共享, 共同应对复杂任务或解决问题的团队水平一般认知能力; 其本质在于超越个体局限, 实现群体层面的认知协同与效能提升。然而, 当前该领域研究仍面临概念与测量双重挑战：概念上, 多学科视角并存导致概念界定不一, 共享心智模型、交互记忆系统与互动团队认知等理论缺乏整合框架; 测量方法上, 评估型范式擅整体效能衡量却弱于机制揭示, 诊断型范式强于过程解析但生态效度不足。本研究系统梳理众智的概念演进, 阐释其形成机制的主要理论模型, 并对比评估型与诊断型测量范式。在此基础上, 提出未来应推动测量范式整合、构建多模态动态评估体系, 并加强人智协同团队研究, 以拓展众智的理论边界与应用前景。Abstract
In todays increasingly complex and volatile work environments, teamsnot individualshave emerged as the fundamental units through which organizations navigate uncertainty, solve intricate problems, and drive innovation. Central to this shift is the construct of collective intelligence, defined as the team-level general cognitive ability that enables groups to collaboratively communicate, share knowledge, and effectively address complex tasks. Unlike the sum of individual intelligences, collective intelligence arises from synergistic cognitive processes that transcend individual limitations, resulting in emergent capabilities greater than the mere aggregation of parts. Despite its critical importance, research on collective intelligence has long been hampered by two persistent challenges: (1) conceptual fragmentation due to divergent disciplinary perspectives and the absence of an integrative theoretical framework capable of explaining the processes through which collective intelligence emergesand (2) methodological inconsistency between measurement paradigms that either prioritize outcome-based assessment or process-based diagnosiseach with significant limitations.To advance the field, this study proposes a unified conceptualization: collective intelligence is best understood as an emergent property generated through the dynamic interplay of three core mechanismsshared mental modelsSMMs, transactive memory systems(TMS), and interactive team cognition(ITC). Shared mental models constitute the cognitive foundation of teamwork, providing a common understanding of tasks, roles, and procedures that enables coordination, reduces ambiguity, and enhances predictability in team interactions. Transactive memory systems reflect the distributed nature of team knowledge, functioning as a collective memory architecture wherein members specialize in different domains and rely on one another to access and apply expertise efficiently. This system allows teams to adapt flexibly to complex demands by leveraging the full spectrum of their distributed cognitive resources. Meanwhile, interactive team cognition emphasizes that intelligence is not static or stored solely within individuals, but is continuously co-constructed through real-time communication and interaction. It serves as the active process that animates shared mental models and transactive memory systems, transforming cognitive potential into adaptive, context-sensitive performance.Critically, these three components do not operate in isolation; rather, they form a mutually reinforcing cycle. Shared mental models facilitate smoother interaction and more effective knowledge exchange, which in turn strengthens the transactive memory system. Efficient knowledge distribution enables richer, more informed interactions, further refining shared understanding. It is within this virtuous loopanchored in shared cognition, distributed expertise, and dynamic interactionthat collective intelligence genuinely emerges.Building on this integrative framework and informed by a critical analysis of the limitations inherent in the two dominant measurement paradigms, we identify three pivotal directions for future research. First, there is an urgent need to integrate and optimize measurement paradigms. Traditional assessments often sacrifice process insight for outcome validity, or vice versa. Future studies should design ecologically valid team tasks that simulate real-world complexity while systematically eliciting key collaborative behaviors. By combining performance metrics with rich process datasuch as communication patterns, decision sequences, and problem-solving strategiesresearchers can develop comprehensive measurement frameworks that balance diagnostic precision with external validity.Second, the field should embrace multimodal, dynamic assessment systems. Advances in sensing technologies and computational methods now allow for the simultaneous capture of behavioral, vocal, eye-tracking, physiological synchrony, and even neurocognitive data during team interactions. Integrating these multimodal streams through methods such as machine learning can yield granular, time-sensitive insights into how collective cognition unfolds in real time, moving beyond static snapshots to capture the fluid, emergent nature of collective intelligence.Third, and perhaps most urgently, research should expand to address HumanAI collaborative teams. As artificial intelligence becomes an integral team member in many domains, new questions arise about cognitive division of labor, mutual trust calibration, accountability, and the very nature of shared understanding between humans and intelligent agents. Developing novel theoretical models and methodological tools for these hybrid teams will not only redefine the boundaries of collective intelligence but also ensure its relevance in the age of humanmachine symbiosis. 
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