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.
more »
« less
This content will become publicly available on May 28, 2026
AI-Assisted Coordination of Human Teams in Situated Tasks
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.
more »
« less
- Award ID(s):
- 2205454
- PAR ID:
- 10611951
- Publisher / Repository:
- Association for the Advancement of Artificial Intelligence (AAAI)
- Date Published:
- Journal Name:
- Proceedings of the AAAI Symposium Series
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2994-4317
- Page Range / eLocation ID:
- 104 to 106
- Subject(s) / Keyword(s):
- Human-AI Collaboration AI Coach The Science of Teamwork
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Clarke-Midura, J; Kollar, I; Gu, X; D'Angelo, C (Ed.)TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach.more » « less
-
Clarke-Midura, J; Kollar, I; Gu, X; D’Angelo, C (Ed.)TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach.more » « less
-
Clarke_Midura, J; Kollar, I; Gu, X; D’Angelo, C (Ed.)TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach.more » « less
-
Educational AI (AIEd) systems are increasingly designed and evaluated with an awareness of the hybrid nature of adaptivity in real-world educational settings. In practice, beyond being a property of AIEd systems alone, adaptivity is often jointly enacted by AI systems and human facilitators (e.g., teachers or peers). Despite much recent research activity, theoretical and conceptual guidance for the design of such human–AI systems remains limited. In this paper we explore how adaptivity may be shared across AIEd systems and the various human stakeholders who work with them. Based on a comparison of prior frameworks, which tend to examine adaptivity in AIEd systems or human coaches separately, we first synthesize a set of dimensions general enough to capture human–AI hybrid adaptivity. Using these dimensions, we then present a conceptual framework to map distinct ways in which humans and AIEd systems can augment each other’s abilities. Through examples, we illustrate how this framework can be used to characterize prior work and envision new possibilities for human–AI hybrid approaches in education.more » « less
An official website of the United States government
