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Online Metrics to Enhance Human-Artificial Agent Collaboration Efficiency: A Narrative Literature Review.

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Title: Online Metrics to Enhance Human-Artificial Agent Collaboration Efficiency: A Narrative Literature Review.
Authors: Pinto, Adam H. M.1 (AUTHOR) adam.moreira-pinto@isae-supaero.fr, Lounis, Christophe1 (AUTHOR), Causse, Mickaël1 (AUTHOR), Chanel, Caroline P. C.1 (AUTHOR)
Source: International Journal of Human-Computer Interaction. Jun2026, Vol. 42 Issue 12, p9273-9296. 24p.
Subjects: Human-robot interaction, Behavioral assessment, Artificial intelligence, Adaptive control systems, Group dynamics, Software measurement
Abstract: Machines have traditionally served as tools to fulfill human requirements; however, the rapid advancement of artificial intelligence has enabled the development of autonomous systems capable of functioning as fully integrated teammates. These agents can share information, assume roles, and execute tasks within collaborative environments. Effective Human–Artificial Agent collaboration, achieved through the integration of complementary cognitive and operational capabilities, has demonstrated improvements in overall team performance across multiple domains, including industrial robotics, healthcare, and augmented reality. Nevertheless, achieving both optimal performance and interaction fluency remains a significant challenge. Real-time monitoring of tasks, intentions, and constraints of human and artificial partners is still limited, and the application of quantifiable online metrics for this purpose is underexplored. This narrative review systematically examines online metrics derived from behavioral, physiological, and interaction-based approaches, discussing their potential to enhance adaptive mechanisms and optimize team fluency in H–AA collaboration. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Jun2026, Vol. 42 Issue 12, p9273-9296. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Human-robot+interaction%22">Human-robot interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Group+dynamics%22">Group dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink>
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  Data: Machines have traditionally served as tools to fulfill human requirements; however, the rapid advancement of artificial intelligence has enabled the development of autonomous systems capable of functioning as fully integrated teammates. These agents can share information, assume roles, and execute tasks within collaborative environments. Effective Human–Artificial Agent collaboration, achieved through the integration of complementary cognitive and operational capabilities, has demonstrated improvements in overall team performance across multiple domains, including industrial robotics, healthcare, and augmented reality. Nevertheless, achieving both optimal performance and interaction fluency remains a significant challenge. Real-time monitoring of tasks, intentions, and constraints of human and artificial partners is still limited, and the application of quantifiable online metrics for this purpose is underexplored. This narrative review systematically examines online metrics derived from behavioral, physiological, and interaction-based approaches, discussing their potential to enhance adaptive mechanisms and optimize team fluency in H–AA collaboration. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194574884
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        Value: 10.1080/10447318.2025.2575895
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              Text: Jun2026
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