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 during collaborative problem-solving involving humans, AI, or multiple agents. The framework is designed to address limitations in current analytical approaches and focuses on metacognitive regulatory mechanisms.

What the Document Says

• The authors propose a framework for analyzing dialogues in collaborative problem-solving contexts involving human-AI and multi-agent collaboration.
• The focus is on the emerging dynamics of these partnerships, particularly during problem-solving.
• The proposed framework includes a hierarchical two-layer coding scheme that integrates cognitive and non-cognitive problem-solving with metacognitive regulatory mechanisms.
• The authors claim that this framework overcomes limitations in current analytical approaches.
• The effectiveness and generalizability of the framework are demonstrated across nine datasets from various domains.
• Insights are provided into how humans, AI, and multiple agents coordinate their knowledge, skills, and efforts to solve complex problems.
• The authors suggest that metacognitive regulation is an essential factor in 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

• The proposed framework addresses limitations in current analytical approaches.
• Metacognitive regulation is an essential discriminator of deeper collaboration.

Evidence Used

• Demonstrated effectiveness and generalizability across nine datasets spanning multiple domains

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.