［目的/意义］ 随着人工智能持续嵌入组织工作流程，知识转移正由传统“人—人”互动转向“人—AI—人”的混合协同过程。传统以人为中心的知识转移模式常受认知负荷、信息过载、共享动机不足和隐性知识流失等因素制约，难以充分支撑组织知识资产保存、组织记忆延续和流程创新。因此，本文从知识管理视角构建AI介导知识转移的理论框架，解释AI作为主动性社会技术中介对组织知识捕获、共享、理解与复用过程的重构作用。 ［方法/过程］ 采用文献综述与理论整合方法，基于社会技术系统理论、SECI知识创造模型和能力—动机—机会框架，梳理AI介入知识转移的过程路径、角色重构与运行边界。 ［结果/结论］ 研究指出，AI主要通过知识捕获与编码、匹配与推荐、翻译与适配、整合与复用4个环节，提升知识可达性、可理解性和可复用性，并推动知识工作者承担AI编排与知识守护两类互补性功能角色。由此，进一步提出双元角色重构模型，并从知识管理视角揭示AI介入知识转移的社会技术重构逻辑，为组织构建人机协同知识管理体系、保存组织记忆并推动持续流程创新提供理论参考。

[Purpose/Significance] As artificial intelligence (AI) becomes increasingly embedded in organizational workflows, knowledge transfer is shifting from traditional human-to-human interaction toward a hybrid collaborative process of "human-AI-human." Conventional human-centric knowledge transfer models are often constrained by factors such as cognitive load, information overload, insufficient sharing motivation, and tacit knowledge loss, making it difficult to adequately support the preservation of organizational knowledge assets, the continuity of organizational memory, and process innovation. Therefore, this study constructs a theoretical framework for AI-mediated knowledge transfer from the perspective of knowledge management, explaining how AI, as a proactive sociotechnical intermediary, restructures the organizational processes of knowledge capture, sharing, comprehension, and reuse. [Method/Process] Employing a literature review and theoretical integration approach, and grounded in sociotechnical systems theory, the SECI model of knowledge creation, and the ability-motivation-opportunity (AMO) framework, this study systematically examined the process pathways, role reconfiguration, and operational boundaries of AI intervention in knowledge transfer. [Results/Conclusion] The findings indicate that AI primarily enhances knowledge accessibility, comprehensibility, and reusability through four key processes: knowledge capture and codification, matching and recommendation, translation and adaptation, and integration and reuse. Concurrently, it drives knowledge workers to assume two complementary functional roles: AI orchestration and knowledge stewardship. Building upon this, the study further proposes a dual-role reconfiguration model and, from a knowledge management perspective, reveals the sociotechnical restructuring logic underlying AI intervention in knowledge transfer, thereby offering theoretical guidance for organizations to construct human-AI collaborative knowledge management systems, preserve organizational memory, and foster continuous process innovation.