Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
It provides a generalizable analytical tool for researchers studying human-AI and multi-agent collaboration, addressing limitations in current dialogue analysis approaches.
The paper introduces a hierarchical two-layer coding scheme for analyzing dialogue in collaborative problem-solving, integrating cognitive and metacognitive dimensions. The framework is validated across nine datasets, showing that metacognitive regulation is a key discriminator of deeper collaboration.
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.