Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System

TL;DR

This study explores how physiological and conversational dynamics in medical teams using an intelligent tutoring system reveal moments of shared discovery and uncertainty, advancing understanding of collaborative problem-solving.

Contribution

It demonstrates how physiological synchrony and semantic analysis can identify critical moments in team collaboration during medical diagnosis tasks.

Findings

High physiological synchrony correlates with lower semantic similarity.

Shared discovery moments involve synchronized physiological signals.

Unsuccessful teams peak during moments of shared uncertainty.

Abstract

Effective collaboration requires teams to manage complex cognitive and emotional states through Socially Shared Regulation of Learning (SSRL). Physiological synchrony (i.e., longitudinal alignment in physiological signals) can indicate these states, but is hard to interpret on its own. We investigate the physiological and conversational dynamics of four medical dyads diagnosing a virtual patient case using an intelligent tutoring system. Semantic shifts in dialogue were correlated with transient physiological synchrony peaks. We also coded utterance segments for SSRL and derived cosine similarity using sentence embeddings. The results showed that activating prior knowledge featured significantly lower semantic similarity than simpler task execution. High physiological synchrony was associated with lower semantic similarity, suggesting that such moments involve exploratory and varied language use. Qualitative analysis triangulated these synchrony peaks as ``pivotal moments'': successful teams synchronized during shared discovery, while unsuccessful teams peaked during shared uncertainty. This research advances human-centered AI by demonstrating how biological signals can be fused with dialogues to understand critical moments in problem solving.