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

Xiaoshan Huang, Conrad Borchers, Jiayi Zhang, Susanne P. Lajoie

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.

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

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.

Paper Structure

This paper contains 15 sections, 2 figures.

Table of Contents

  1. Introduction & Related Work
  2. Semantic Embeddings for Cognitive Processes
  3. Physiological Synchrony for Shared Affective Engagement
  4. Methodology
  5. Study Context and Sample
  6. Data Processing and Analyses
  7. Semantic Analysis and PS.
  8. Mixed-Method Bio-Semantic Mapping.
  9. Results
  10. Mapping Physiological and Semantic Patterns with SSRL
  11. Qualitative Inspection on Pivotal Moments during PS Peaks
  12. Discussion and Implications
  13. Identifying Maladaptive SSRL Behaviors
  14. Toward a Multimodal Bio-Responsive Intelligent Tutoring
  15. Summary and Conclusions

Figures (2)

  • Figure 1: Within-speaker similarity patterns. Left: Mean cosine similarity (±95% CI) for the five most frequent SSRL codes, revealing systematic differences in internal consistency across interaction types. Right: Association between physiological synchrony peaks ($r_{max}$) and cosine similarity, with SSRL code means and 95% CI error bars.
  • Figure 2: Between-speaker similarity patterns. Task execution (TE)maintains higher similarity values, while prior knowledge activation (PKA) shows relatively lower alignment.