Autonomy as a Teammate: Evaluation of Teammate-Likeness

Tokadli, Guliz; Dorneich, Michael C.

doi:10.1177/15553434221108002

Cited by 4 publications

(11 citation statements)

References 47 publications

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“…Twenty-two requirements were described for future space operations with HAT (Tokadlı and Dorneich, 2018). The summative evaluation (Step 4) investigated the factors contributing to the human teammate's perception of an autonomous system (Tokadlı and Dorneich, 2022). In this study, participants evaluated two different CA feedback modalities.…”

Section: Resultsmentioning

confidence: 99%

“…However, in high workload conditions, the opposite occurred. Based on participants' feedback, transparency level with distributed modality, the participants had better idea regarding what the CA was performing and why, and this information help them to move faster instead of trying to figure out what CA was doing in centralized modality (Tokadlı and Dorneich, 2022). While centralized feedback had some benefits during low task load, it was at a significant disadvantage at high task load, where participants increasingly leveraged the distributed feedback mode to track CA actions.…”

Section: Resultsmentioning

confidence: 99%

“…Some studies recommended that the autonomous system should have human-like behaviors and anthropomorphic features (Zhang et al, 2021;Ulusoy and Reisman, 2022). Similarly, the evaluations of case studies showed that the human teammates' perception of teammate-likeness increased with an embodied autonomous teammate that provided cues of actions taken (Tokadlı et al, 2021a;Tokadlı and Dorneich, 2022).…”

Section: Recent Hat Empirical Study Literaturementioning

confidence: 91%

“…The previous studies investigated Single Pilot Operations/ Reduced Pilot Operations (SPO/RCO) in commercial aviation (see details of case study from Tokadlı et al (2021a); Tokadlı et al (2021b)) and space operations beyond LEO (see details of case study from Tokadlı and Dorneich. (2018); Tokadlı and Dorneich, (2022)) as future aerospace operations with HAT concepts. These case studies were selected because they represented differences in the integration of an autonomous system in the team composition.…”

Section: Summary Of Case Studiesmentioning

confidence: 99%

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Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements

Tokadli

Dorneich

2023

Front. Aerosp. Eng.

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This paper developed human-autonomy teaming (HAT) characteristics and requirements by comparing and synthesizing two aerospace case studies (Single Pilot Operations/Reduced Crew Operations and Long-Distance Human Space Operations) and the related recent HAT empirical studies. Advances in sensors, machine learning, and machine reasoning have enabled increasingly autonomous system technology to work more closely with human(s), often with decreasing human direction. As increasingly autonomous systems become more capable, their interactions with humans may evolve into a teaming relationship. However, humans and autonomous systems have asymmetric teaming capabilities, which introduces challenges when designing a teaming interaction paradigm in HAT. Additionally, developing requirements for HAT can be challenging for future operations concepts, which are not yet well-defined. Two case studies conducted previously document analysis of past literature and interviews with subject matter experts to develop domain knowledge models and requirements for future operations. Prototype delegation interfaces were developed to perform summative evaluation studies for the case studies. In this paper, a review of recent literature on HAT empirical studies was conducted to augment the document analysis for the case studies. The results of the two case studies and the literature review were compared and synthesized to suggest the common characteristics and requirements for HAT in future aerospace operations. The requirements and characteristics were grouped into categories of team roles, autonomous teammate types, interaction paradigms, and training. For example, human teammates preferred the autonomous teammate to have human-like characteristics (e.g., dialog-based conversation, social skills, and body gestures to provide cue-based information). Even though more work is necessary to verify and validate the requirements for HAT development, the case studies and recent empirical literature enumerate the types of functions and capabilities needed for increasingly autonomous systems to act as a teammate to support future operations.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Recent Hat Empirical Study Literaturementioning

confidence: 91%

Section: Summary Of Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements

Tokadli

Dorneich

2023

Front. Aerosp. Eng.

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“…The method uses machine learning to classify the decision strategy based on behavioral data collected from the human teammate. Tokadlı and Dorneich (2022) explore the factors relevant to people viewing an autonomous system as a "teammate." They find that providing visible cues of the agent's actions increased human perception of teammate likeness and improved performance under high task loads.…”

mentioning

confidence: 99%

Special Issue on Human-AI Teaming and Special Issue on AI in Healthcare

Endsley

Cooke

McNeese

et al. 2022

Journal of Cognitive Engineering and Decision Making

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Navigating virtual teams in generative AI-led learning: The moderation of team perceived virtuality

Darban

2024

Educ Inf Technol

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This study advances the understanding of Artificial Intelligence (AI)’s role, particularly that of conversational agents like ChatGPT, in augmenting team-based knowledge acquisition in virtual learning settings. Drawing on human-AI teams and anthropomorphism theories and addressing the gap in the literature on human-AI collaboration within virtual teams, this study examines a multi-level, longitudinal model using a sample of 344 graduate students from 48 student project teams in online project-based learning environments. Our model investigates the direct and interactional effects of AI characteristics —autonomy and explainability— and team perceived virtuality (TPV) on the learners’ knowledge-updating process. Findings indicate that embedding AI in learning teams supports knowledge acquisition and learning intentions. The results reveal that while AI explainability significantly enhances knowledge update perceptions, AI autonomy alone does not directly influence knowledge acquisition. Instead, the positive effect of AI autonomy on knowledge updating is contingent upon a high TPV within the team. These findings offer new theoretical insights into AI’s empowering role in educational contexts and provide practical guidance for integrating AI into virtual team learning. This research underlines the importance of designing AI tools with a focus on explainability and leveraging the synergy between AI autonomy and TPV to maximize learning outcomes.

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Autonomy as a Teammate: Evaluation of Teammate-Likeness

Cited by 4 publications

References 47 publications

Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements

Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements

Special Issue on Human-AI Teaming and Special Issue on AI in Healthcare

Navigating virtual teams in generative AI-led learning: The moderation of team perceived virtuality

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