English

Socratic: Enhancing Human Teamwork via AI-enabled Coaching

Artificial Intelligence 2025-02-26 v1 Human-Computer Interaction Machine Learning Multiagent Systems

Abstract

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-time team coaching. Specifically, we introduce Socratic, a novel AI system that complements human coaches by providing real-time guidance during task execution. Socratic monitors team behavior, detects misalignments in team members' shared understanding, and delivers automated interventions to improve team performance. We validated Socratic through two human subject experiments involving dyadic collaboration. The results demonstrate that the system significantly enhances team performance with minimal interventions. Participants also perceived Socratic as helpful and trustworthy, supporting its potential for adoption. Our findings also suggest promising directions both for AI research and its practical applications to enhance human teamwork.

Keywords

Cite

@article{arxiv.2502.17643,
  title  = {Socratic: Enhancing Human Teamwork via AI-enabled Coaching},
  author = {Sangwon Seo and Bing Han and Rayan E. Harari and Roger D. Dias and Marco A. Zenati and Eduardo Salas and Vaibhav Unhelkar},
  journal= {arXiv preprint arXiv:2502.17643},
  year   = {2025}
}

Comments

Extended version of an identically-titled paper accepted at AAMAS 2025

R2 v1 2026-06-28T21:56:22.536Z