连接对话与思考:理解协作问题解决中的对话动态
评分
52精选理由
论文提出了原创性高的分析框架,数据集覆盖面广,且对协作智能领域具有直接应用价值。
AI 点评
「该框架首次系统性地将元认知调节纳入对话分析,为设计更高效的协作人工智能和评估人机团队表现提供了可操作的理论基础。」
速览
本文提出一个概念框架,用于分析协作问题解决中的对话动态,重点是人类-AI及多智能体协作的新兴互动。通过双层编码方案整合认知与非认知问题解决及元认知调节机制,在九个跨领域数据集上验证有效性,揭示元认知调节是深层协作的关键区分因素。
- 提出整合认知、非认知与元认知调节的双层编码分析框架
- 在九个跨领域数据集上验证框架的通用性与有效性
- 发现元认知调节是区分浅层与深层协作的关键指标
- 为优化人机协作与多智能体系统提供理论工具
正文
Computer Science > Computation and Language
arXiv:2606.27233v1 (cs) [Submitted on 25 Jun 2026]Title:Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
Authors:Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan, Nancy F. Chen View a PDF of the paper titled Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts, by Zhengyuan Liu and 3 other authors View PDF HTML (experimental)Abstract:We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaboration. As intelligent systems become active agents capable of autonomous reasoning and strategic cooperation, understanding the dialogic interaction during collaborative problem solving is increasingly important for optimizing and evaluating such partnerships. Our framework addresses key limitations in current analytical approaches through a hierarchical two-layer coding scheme that integrates cognitive and non-cognitive problem solving with metacognitive regulatory mechanisms. We demonstrate its effectiveness and generalizability across nine datasets spanning multiple domains, and provide insights into how humans and agents coordinate their knowledge, skills, and efforts to solve complex problems, showing in particular that metacognitive regulation can be an essential discriminator of deeper collaboration.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.27233 [cs.CL] |
| (or arXiv:2606.27233v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27233 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Zhengyuan Liu [view email][v1] Thu, 25 Jun 2026 16:20:49 UTC (8,374 KB)
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