数据智能时代，人工智能在知识创造中的作用对科教创新和社会发展至关重要。人智知识共创过程中，个体或团队可以利用AI智能体的技术能力和社会功能，赋能信息共享、认知整合与决策协商的过程，提升知识生产的效率并推动知识创新。本课题旨在融合信息行为、意义建构、认知科学与社会技术系统理论，采用多种研究方法和技术工具，探索在教育、科研、创意等领域人智知识共创的协同过程，重点揭示AI智能体的动态角色适配和人智协同机制。通过理论调研整合、方法技术研发、多情境实证检验（在个体和群体两个层面）与系统设计评估五个递进的子研究，探究知识共创过程中人智认知对齐的条件与机制、动态角色建模、人智信任演化和权力结构重构，以及知识共创的效果评估。研究结果将为构建有效的人智共创知识生态系 统提供理论框架、技术工具与治理策略，助力教育智能化升级、科研范式转型与创新生态优化，并为数据智能时代知识生产模式变革提供科学依据与实践指南。

       In the era of data intelligence, the role of artificial intelligence (AI) in knowledge creation is crucial to scientific, technological innovation, and social development. During the human-AI knowledge co-creation process, individuals or teams can leverage the technical capabilities and social functions of AI agents to enhance information sharing, knowledge integration, and decision-making, thereby improving the efficiency of knowledge production and promoting knowledge innovation. This project builds upon prior research in information behavior, sensemaking theories, cognitive science, and socio-technical systems theory, adopting a mixed-methods research approach to explore the collaborative process of human-AI knowledge co-creation across various areas, including education, research, and creative design. It will focus on revealing the dynamic impact of AI agents’ role adaptation and human-AI collaboration mechanism on an individual’s cognitive sensemaking process and group collaboration patterns and outcomes. This multi-phase investigation will include five progressively structured sub-study designs, including theoretical research and integration, methodology research and technology development, multi-scenario empirical user studies (at both individual level and group level), system design, implementation, and evaluation. The project will tackle the critical questions of human-AI cognitive alignment mechanisms, dynamic role modeling and adaptation, trust evolvement and power structure reconstruction in human-AI negotiation, and evaluation of human-AI collaboration results. The findings will help provide a comprehensive theoretical framework, technical tools, and governance strategies for building an efficient human-AI collaborative knowledge ecosystem for knowledge co-creation. They will also shed light on the AI empowerment and transformation of research paradigms, reform of education, and the optimization of innovation ecosystems, to provide a theoretical basis and a practical guide for the transformation of knowledge production paradigms in the era of data intelligence.