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

Researchers present a framework for analyzing dialogues during collaborative problem-solving tasks involving human-AI and multi-agent interactions. The framework aims to optimize and evaluate these partnerships by focusing on metacognitive regulatory mechanisms.

What the Document Says

• The paper presents a framework for analyzing dialogue in collaborative problem-solving contexts with human-AI and multi-agent collaboration as focus.
• The framework is built upon a hierarchical two-layer coding scheme that integrates cognitive and non-cognitive problem solving with metacognitive regulatory mechanisms.
• The researchers demonstrate the effectiveness and generalizability of their framework across nine datasets from various domains.
• The study shows that metacognitive regulation can be a key factor in deeper collaboration between humans and agents.

Key Concepts Mentioned

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

Claims Made by the Document

• The presented framework addresses key limitations in current analytical approaches.
• Metacognitive regulation is essential for deeper collaboration in human-agent partnerships.

Evidence Used

• Nine datasets spanning multiple domains were used to demonstrate the effectiveness and generalizability of the framework.

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.