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Effective teamwork is crucial in high-stakes domains, yet it is highly challenging to achieve. Team members often must make decisions with limited information and under constraints on communication and time. Recognizing both the value of human coaches as well as the challenges of integrating them into practical settings, we envision AI-based coaching agents to enhance team coordination and performance. This extended abstract introduces AI Coaches and Coordinators, highlights key research questions from both human and AI perspectives that must be addressed to realize them, and summarizes our recent work in developing algorithms and systems to bring AI Coaches and Coordinators to fruition.
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This content will become publicly available on June 5, 2026
Socratic: Enhancing Human Teamwork via AI-enabled Coaching
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.
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- Award ID(s):
- 2205454
- PAR ID:
- 10615487
- Publisher / Repository:
- Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
- Date Published:
- ISBN:
- 9798400714269
- Page Range / eLocation ID:
- 1876–1885
- Subject(s) / Keyword(s):
- Decision Support Imitation Learning Mental Models Teamwork
- Format(s):
- Medium: X
- Location:
- Detroit, MI, USA
- Sponsoring Org:
- National Science Foundation
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