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

arXiv: https://arxiv.org/abs/2603.29950 · HTML: https://mlsanity.com/paper/2603.29950/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Disentangling Learning from Judgment: Representation Learning for Open Response Analytics](https://mlsanity.com/paper/2512.23941/) (2025)
- [What Makes Teamwork Work? A Multimodal Case Study on Emotions and Diagnostic Expertise in an Intelligent Tutoring System](https://mlsanity.com/paper/2505.00948/) (2025)
- [Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation](https://mlsanity.com/paper/2510.16633/) (2025)
- [Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learning](https://mlsanity.com/paper/2601.04458/) (2026)

## Abstract (arXiv)

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.