# Socratic: Enhancing Human Teamwork via AI-enabled Coaching

arXiv: https://arxiv.org/abs/2502.17643 · HTML: https://mlsanity.com/paper/2502.17643/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 96 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models](https://mlsanity.com/paper/2109.14700/) (2021)
- [Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies](https://mlsanity.com/paper/2001.03231/) (2020)
- [Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare](https://mlsanity.com/paper/2102.08507/) (2021)
- [Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions](https://mlsanity.com/paper/1707.07399/) (2017)
- [Automated Task-Time Interventions to Improve Teamwork using Imitation Learning](https://mlsanity.com/paper/2303.00413/) (2023)
- [IDIL: Imitation Learning of Intent-Driven Expert Behavior](https://mlsanity.com/paper/2404.16989/) (2024)

## Built on by (most cited first, 2 of 2)

- [Boosting Team Modeling through Tempo-Relational Representation Learning](https://mlsanity.com/paper/2507.13305/) (2025)
- [Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications](https://mlsanity.com/paper/2602.18582/) (2026)

## 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.