Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System
This research advances human-centered AI by fusing biological signals with dialogues to understand critical moments in problem-solving for medical teams, though it is incremental as it builds on existing methods for analyzing team dynamics.
The study investigated how physiological synchrony and semantic patterns in dialogue relate to team problem-solving in medical dyads using an intelligent tutoring system, finding that high physiological synchrony correlated with lower semantic similarity, indicating exploratory language use during pivotal moments like shared discovery or uncertainty.
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.