Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan, Nancy F. Chen
• Venue: arXiv: cs.CL
• Published: 2026-06-25
• Collected: 2026-06-26
• URL: http://arxiv.org/abs/2606.27233v1
• PDF: https://arxiv.org/pdf/2606.27233v1
• DOI: UNKNOWN

Plain-Language Brief

The authors present a framework for analyzing dialogue in collaborative problem-solving contexts, especially in human-AI and multi-agent collaboration. The framework aims to understand the emerging dynamics of these partnerships by integrating cognitive and non-cognitive problem-solving with metacognitive regulatory mechanisms.

What the Document Says

• The authors present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts.
• The focus is on human-AI and multi-agent collaboration.
• The framework integrates cognitive and non-cognitive problem solving with metacognitive regulatory mechanisms.
• The effectiveness of the framework is demonstrated across nine datasets spanning multiple domains.
• The framework provides insights into how humans and agents coordinate their knowledge, skills, and efforts to solve complex problems.
• Metacognitive regulation is shown to be an essential discriminator of deeper collaboration.

Key Concepts Mentioned

• dialogue analysis
• collaborative problem-solving
• human-AI collaboration
• multi-agent collaboration
• cognitive problem solving
• non-cognitive problem solving
• metacognitive regulatory mechanisms

Claims Made by the Document

• Understanding dialogic interaction during collaborative problem solving is important for optimizing and evaluating human-AI and multi-agent partnerships.
• The proposed framework addresses key limitations in current analytical approaches.

Evidence Used

• The authors demonstrate the effectiveness of their framework across nine datasets.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.