北京大学学报（自然科学版） ›› 2026, Vol. 62 ›› Issue (4): 924-940.DOI: 10.13209/j.0479-8023.2026.023

上一篇    

人工智能将全面取代人类吗? Ⅱ. 基于任务过程的人智(HI-AI)分工与协作模式

王海珍1, 张燕2,†   

  1. 1. 西安外国语大学商学院, 西安 710023 2. 北京大学心理与认知科学学院, 生物与机器智能教育部重点实验室, 脑机接口与心理健康调控北京市重点实验室, 北京 100871
  • 收稿日期:2025-12-31 修回日期:2026-04-17 出版日期:2026-07-20 发布日期:2026-07-20
  • 基金资助:
    国家自然科学基金(72472003)和教育部人文社会科学基金(22YJC630130)资助

Will Artificial Intelligence (AI) Completely Replace Humans? II. Task-Process-Based Model of Human-AI Division of Labor and Collaboration

WANG Haizhen1, ZHANG Yan2,†   

  1. 1. School of Business, Xi’an International Studies University, Xi’an 710023 2. Key Laboratory of Machine Perception (Ministry of Education), Beijing Key Laboratory of Brain-Computer Interface and Mental Health Modulation, School of Psychological and Cognitive Sciences, Peking University, Beijing 100871
  • Received:2025-12-31 Revised:2026-04-17 Online:2026-07-20 Published:2026-07-20

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摘要：

针对当前人智协作研究中存在的协作模式体系碎片化、未能与任务特征系统关联的问题, 构建以任务过程模型为基础的模块化人智协作设计框架, 针对10项关键任务特征, 提出覆盖任务输入、处理和输出3个环节的11种基础协作子模式, 并明确其组合规则。以新产品开发和多学科诊疗等6个典型团队任务为例, 演示如何根据具体任务在多环节、多维度上的特征, 依次选择协作子模式。通过提出全过程、多特征、可组合的协作模式, 整合碎片化的协作模式, 将协作设计推进至操作化层面, 为组织系统规划和优化人智协作工作流程提供理论支撑与实践路径。

关键词: 人智协作, 任务特征, 协作模式, 任务过程模型, 人工智能

Abstract:

Current human-AI collaboration (HAIC) research suffers from fragmented collaborative taxonomies and a lack of systematic alignment with task characteristics. To address these gaps, this study constructs a modular HAIC design framework grounded in the task-process model. On the basis of analyzing ten critical task characteristics, the framework defines eleven fundamental collaborative sub-patterns spanning task input, processing, and output stages, while clarifying their underlying combination rules. Finally, utilizing six representative team tasks — such as new product development and multidisciplinary consultation — we demonstrate how to sequentially select and adapt collaborative sub-patterns based on specific multi-stage and multi-dimensional task features. By providing a comprehensive, multi-feature, and modular theoretical collaboration framework, this research integrates fragmented collaborative models and advances HAIC design to an operational level. These findings offer both theoretical support and a practical roadmap for organizations to systematically plan and optimize human-AI collaborative workflows. 

Key words: human-AI collaboration (HAIC), task characteristics, collaborative patterns, task-process model; artificial intelligence

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