What Makes Teamwork Work? A Multimodal Case Study on Emotions and Diagnostic Expertise in an Intelligent Tutoring System

TL;DR

This study investigates how emotions and professional expertise interplay during group medical diagnosis within an Intelligent Tutoring System. Using a multimodal case study of four female medical professional dyads, it analyzes verbal sentiment and physiological heart-rate changes to map the team emotional climate and its relation to diagnostic efficiency, grounded in the socially shared regulation of learning framework. Key findings show that socio-motivational interactions drive a more positive emotional climate, and that emotional fluctuations can either catalyze productive knowledge exchange in high-performing teams or correlate with unresolved reasoning and misdiagnosis in lower-performing teams. The work highlights the potential of affect-aware ITS design, where emotion-detection and adaptive prompts support better collaboration and diagnostic accuracy in high-stakes medical contexts.

Abstract

Teamwork is pivotal in medical teamwork when professionals with diverse skills and emotional states collaborate to make critical decisions. This case study examines the interplay between emotions and professional skills in group decision-making during collaborative medical diagnosis within an Intelligent Tutoring System (ITS). By comparing verbal and physiological data between high-performing and low-performing teams of medical professionals working on a patient case within the ITS, alongside individuals' retrospective collaboration experiences, we employ multimodal data analysis to identify patterns in team emotional climate and their impact on diagnostic efficiency. Specifically, we investigate how emotion-driven dialogue and professional expertise influence both the information-seeking process and the final diagnostic decisions. Grounded in the socially shared regulation of learning framework and utilizing sentiment analysis, we found that social-motivational interactions are key drivers of a positive team emotional climate. Furthermore, through content analysis of dialogue and physiological signals to pinpoint emotional fluctuations, we identify episodes where knowledge exchange and skill acquisition are most likely to occur. Our findings offer valuable insights into optimizing group collaboration in medical contexts by harmonizing emotional dynamics with adaptive strategies for effective decision-making, ultimately enhancing diagnostic accuracy and teamwork effectiveness.

Figures (1)

• Figure 1: Interaction by team climate and compound sentiment by interaction.