AHCI RESEARCH GROUP

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OUR RESEARCH

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2025

Volkova, S.; Nguyen, D.; Penafiel, L.; Kao, H. -T.; Cohen, M.; Engberson, G.; Cassani, L.; Almutairi, M.; Chiang, C.; Banerjee, N.; Belcher, M.; Ford, T. W.; Yankoski, M. G.; Weninger, T.; Gomez-Zara, D.; Rebensky, S.

VirTLab: Augmented Intelligence for Modeling and Evaluating Human-AI Teaming Through Agent Interactions Proceedings Article

In: R.A., Sottilare; J., Schwarz (Ed.): Lect. Notes Comput. Sci., pp. 279–301, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303192969-4 (ISBN).

Abstract | Links | BibTeX | Tags: Agent based simulation, agent-based simulation, Augmented Reality, Causal analysis, HAT processes and states, Human digital twin, human digital twins, Human-AI team process and state, Human-AI teaming, Intelligent virtual agents, Operational readiness, Personnel training, Team performance, Team process, Virtual teaming, Visual analytics

@inproceedings{volkova_virtlab_2025,

title = {VirTLab: Augmented Intelligence for Modeling and Evaluating Human-AI Teaming Through Agent Interactions},

author = {S. Volkova and D. Nguyen and L. Penafiel and H. -T. Kao and M. Cohen and G. Engberson and L. Cassani and M. Almutairi and C. Chiang and N. Banerjee and M. Belcher and T. W. Ford and M. G. Yankoski and T. Weninger and D. Gomez-Zara and S. Rebensky},

editor = {Sottilare R.A. and Schwarz J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007830752&doi=10.1007%2f978-3-031-92970-0_20&partnerID=40&md5=c578dc95176a617f6de2a1c6f998f73f},

doi = {10.1007/978-3-031-92970-0_20},

isbn = {03029743 (ISSN); 978-303192969-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15813 LNCS},

pages = {279–301},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This paper introduces VirTLab (Virtual Teaming Laboratory), a novel augmented intelligence platform designed to simulate and analyze interactions between human-AI teams (HATs) through the use of human digital twins (HDTs) and AI agents. VirTLab enhances operational readiness by systematically analyzing HAT dynamics, fostering trust development, and providing actionable recommendations to improve team performance outcomes. VirTLab combines agents driven by large language models (LLM) interacting in a simulated environment with integrated HAT performance measures obtained using interactive visual analytics. VirTLab integrates four key components: (1) HDTs with configurable profiles, (2) operational AI teammates, (3) a simulation engine that enforces temporal and spatial environment constraints, ensures situational awareness, and coordinates events between HDT and AI agents to deliver high-fidelity simulations, and (4) an evaluation platform that validates simulations against ground truth and enables exploration of how HDTs and AI attributes influence HAT functioning. We demonstrate VirTLab’s capabilities through focused experiments examining how variations in HDT openness, agreeableness, propensity to trust, and AI reliability and transparency influence HAT performance. Our HAT performance evaluation framework incorporates both objective measures such as communication patterns and mission completion, and subjective measures to include perceived trust and team coordination. Results on search and rescue missions reveal that AI teammate reliability significantly impacts communication dynamics and team assistance behaviors, whereas HDT personality traits influence trust development and team coordination -insights that directly inform the design of HAT training programs. VirTLab enables instructional designers to explore interventions in HAT behaviors through controlled experiments and causal analysis, leading to improved HAT performance. Visual analytics support the examination of HAT functioning across different conditions, allowing for real-time assessment and adaptation of scenarios. VirTLab contributes to operational readiness by preparing human operators to work seamlessly with AI counterparts in real-world situations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Agent based simulation, agent-based simulation, Augmented Reality, Causal analysis, HAT processes and states, Human digital twin, human digital twins, Human-AI team process and state, Human-AI teaming, Intelligent virtual agents, Operational readiness, Personnel training, Team performance, Team process, Virtual teaming, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

This paper introduces VirTLab (Virtual Teaming Laboratory), a novel augmented intelligence platform designed to simulate and analyze interactions between human-AI teams (HATs) through the use of human digital twins (HDTs) and AI agents. VirTLab enhances operational readiness by systematically analyzing HAT dynamics, fostering trust development, and providing actionable recommendations to improve team performance outcomes. VirTLab combines agents driven by large language models (LLM) interacting in a simulated environment with integrated HAT performance measures obtained using interactive visual analytics. VirTLab integrates four key components: (1) HDTs with configurable profiles, (2) operational AI teammates, (3) a simulation engine that enforces temporal and spatial environment constraints, ensures situational awareness, and coordinates events between HDT and AI agents to deliver high-fidelity simulations, and (4) an evaluation platform that validates simulations against ground truth and enables exploration of how HDTs and AI attributes influence HAT functioning. We demonstrate VirTLab’s capabilities through focused experiments examining how variations in HDT openness, agreeableness, propensity to trust, and AI reliability and transparency influence HAT performance. Our HAT performance evaluation framework incorporates both objective measures such as communication patterns and mission completion, and subjective measures to include perceived trust and team coordination. Results on search and rescue missions reveal that AI teammate reliability significantly impacts communication dynamics and team assistance behaviors, whereas HDT personality traits influence trust development and team coordination -insights that directly inform the design of HAT training programs. VirTLab enables instructional designers to explore interventions in HAT behaviors through controlled experiments and causal analysis, leading to improved HAT performance. Visual analytics support the examination of HAT functioning across different conditions, allowing for real-time assessment and adaptation of scenarios. VirTLab contributes to operational readiness by preparing human operators to work seamlessly with AI counterparts in real-world situations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.