人—AI协同

Human–AI Collaboration

马鸿佳,韩姝婷

2026 第 48 卷 第 07 期 页码 3-21

DOI: 10.16538/j.cnki.fem.20260516.401

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摘要

数字经济背景下,人工智能深度嵌入组织流程,人—AI协同逐渐成为学界和业界共同关注的前沿议题。尽管近年来学术界对人—AI协同的研究兴趣日益浓厚,但现有成果仍较为零散,缺乏系统整合。基于此,本文以220篇人—AI协同的英文文献为研究对象,利用BERTopic主题模型聚合出13个主题,并结合人工编码与层次聚类可视化结果,将研究内容归纳为四个方面,即内涵和构成、前因、结果与边界条件。在此基础上,本文进一步对相关中英文文献进行了系统化梳理,构建了人—AI协同的整合研究框架,并在该框架之上提出了未来研究展望,包括深化人—AI协同的内涵并揭示其构成的动态作用机制、拓展前因并揭示多要素的协同作用、延伸结果维度并跨层次探讨结果的联动机制、推进边界条件研究,以及丰富数据来源、研究方法。本文对人—AI协同研究现有文献进行了系统性归纳与整合,旨在为后续学术探索提供知识基础与研究参考。

In the context of the digital economy, artificial intelligence (AI) is deeply embedded in organizational processes, making human–AI collaboration an emerging issue of growing interest among both scholars and practitioners. However, existing research remains fragmented and lacks systematic integration. To address this gap, this paper analyzes 220 English-language articles on human–AI collaboration. Using the BERTopic topic model, it identifies 13 research topics and, in combination with manual coding and hierarchical clustering visualizations, synthesizes the literature into four major domains: conceptualization and components, antecedents, outcomes, and boundary conditions. Building on this classification, this paper further conducts a systematic review of both English- and Chinese-language studies to develop an integrative research framework for human–AI collaboration. On this basis, it outlines future research directions, including deepening the understanding of human–AI collaboration and elucidating the dynamic mechanisms among its components, extending research on antecedents and revealing the joint effects of multiple elements, broadening outcome dimensions and examining cross-level linkages among outcomes, advancing research on boundary conditions, and enriching data sources and research methods. By systematically organizing and integrating the existing literature, this paper aims to provide a solid knowledge base and useful reference for future research on human–AI collaboration.

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