mlsanityData to 7 Oct 2026

Socratic: Enhancing Human Teamwork via AI-enabled Coaching

arXiv 2502.17643 ↗First posted 24 Feb 2025cs.AI cs.HC cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 96 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

8 of 96 references.

References · 8

  1. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 225
  2. 2019Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Gamescited by 37
  3. 2021Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Modelscited by 21
  4. 2020Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studiescited by 12
  5. 2021Towards an AI Coach to Infer Team Mental Model Alignment in Healthcarecited by 11
  6. 2017Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actionscited by 8
  7. 2023Automated Task-Time Interventions to Improve Teamwork using Imitation Learningcited by 7
  8. 2024IDIL: Imitation Learning of Intent-Driven Expert Behaviorcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.