ArXiv

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

2026-06-25 · 16:20 UTC ·Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan... ·1 min read
Authors
Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan...
Categories
cs.CL, cs.AI
arXiv
https://arxiv.org/abs/2606.27233v1
PDF
https://arxiv.org/pdf/2606.27233v1

Brief

The authors present a conceptual framework and hierarchical two-layer coding scheme to analyze dialogue during collaborative problem solving, integrating cognitive, non-cognitive, and metacognitive regulatory processes. Applied to nine cross-domain datasets, the approach uncovers how humans and autonomous agents coordinate knowledge and effort, showing metacognitive regulation as a key marker of deeper collaboration. Full text was not available for review; summary is based on the abstract.

Why it matters

Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan, and Nancy F. Chen (published 2026-06-25) introduce a hierarchical two-layer coding scheme that integrates cognitive and non-cognitive problem solving with explicit metacognitive regulatory mechanisms.

Key details

  • They validate the framework across nine datasets spanning multiple domains and report that metacognitive regulation reliably discriminates deeper forms of human–AI and multi-agent collaboration.
Source evidence

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.