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Article published In: Multidisciplinary Perspectives on Human-AI Team Trust
Edited by Nicolo' Brandizzi, Morgan Elizabeth Bailey, Carolina Centeio Jorge, Myke C. Cohen, Francesco Frattolillo and Alan Richard Wagner
[Interaction Studies 26:2] 2025
► pp. 267–297

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Exploratory models of human-AI teams

Leveraging human digital twins to investigate trust development

ORCiD logo with link | Aptima, Inc.
 | Aptima, Inc. | Arizona State University
 | Aptima, Inc.
 | Aptima, Inc.
 | Aptima, Inc.
 | Aptima, Inc.
 | Aptima, Inc.
 | Aptima, Inc.
 | Aptima, Inc.
Published online: 27 February 2026
DOI logo with link
https://doi.org/10.1075/is.24052.ngu
Abstract
As human-agent teaming (HAT) research continues to grow, computational methods for modeling HAT behaviors and measuring HAT effectiveness also continue to develop. One rising method involves the use of human digital twins (HDT) to approximate human behaviors and socio-emotional-cognitive reactions to AI-driven agent team members. To help HDT research effectively model human trust in HATs, we offer two lines of insight. First, through a review of the HAT trust literature, we identify key characteristics and attributes of trust that must be considered in order to properly conceptualize, model, and measure trust. Through this review, we outline the theoretical foundations of trust needed for effective HDTs capable of emulating human trust and offer guidance on where and how extant HAT research should translate into HDT modeling and future research. Second, through causal analyses of archival team communication data from a HAT experiment, we supplement theoretical foundations for modeling trust with data-driven insights to guide the trust-related language HDTs may need to effectively emulate human trust. Finally, we discuss implications of these combined theoretical and empirical insights for future HDT research, highlighting the necessity of ongoing validation against human behaviors and the refinement of computational methods. This paper ultimately aims to advance both the fidelity and applicability of HDTs in modeling nuanced human-agent trust dynamics, fostering more effective and realistic human-agent collaborations.
Article outline
  • 1.Introduction
  • 2.Theoretical foundations of trust in HATs
    • 2.1Defining and conceptualizing trust in HDTATs
    • 2.2Critical characteristics of trust for modeling
      • Setting HDT initial trust in AI teammates
      • Changes in trust over time
      • Computational modeling of HDT trust over time
    • 2.3Methods for measuring HAT trust
      • Self-reported trust
      • Behavioral trust
      • Emerging techniques for measuring trust
  • 3.Empirical examination of HAT trust
    • 3.1Data driven insights for modeling HAT trust
      • Measuring HAT trust using causal models
      • Causal analysis results: Empathy constructs
      • Causal analysis results: Socio-cognitive constructs
      • Causal analysis results: Emotional constructs
      • Causal analysis key takeaways
    • 3.2Preliminary modeling of HDT trust
      • Preliminary HDT simulation limitations and implications
    • 3.3Operational implications for defense applications
  • 4.Conclusion
    • Contributions to HAT science
    • Future directions
  • References
References (77)
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