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Team Communication Analysis Framework

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Evidence · 2 claims (2 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description

A Spain et al. framework that transcribes team speech, uses transformer-based LLMs to recognize dialogue acts, and passes communication insights to an adaptation engine for scaffolding military squad-training team members.

Components

  • Team Communication Analysis Framework using transformer-based LLMs for dialogue act recognition with limited training data: The article presents a team communication analysis framework that leverages transformer-based LLMs, particularly GPT models, for dialogue act recognition in scenarios with limited training data. Speech uttered by team members is collected by the learning environment and converted into transcripts using automatic speech recognition; the framework then analyzes the transcripts to recognize dialogue acts, and these insights are passed to an adaptation engine that provides adaptive scaffolding to each team member. The framework is designed to support adaptive team training by enabling instructors and researchers to gain insights into team actions and processes that impact team coordination. (Spain et al. (2024))

Claims

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Key Sources

  • Spain, R., Min, W., Kumaran, V., Pande, V., Saville, J., & Lester, J. (2024). Applying Large Language Models to Enhance Dialogue and Communication Analysis for Adaptive Team Training. https://doi.org/10.21203/rs.3.rs-4565500/v1
Page metadata
Field Value
type product
id team-communication-analysis-framework
title Team Communication Analysis Framework
description A Spain et al.
status review
product_kind framework
generated by process:settle-candidates on 2026-10-28
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