Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective

2025-04-15

AI Summary

Current research on human–autonomy teams (HATs) is highly fragmented, focusing narrowly on isolated phases or singular challenges—such as trust calibration—without a systemic understanding of long-term adaptability. Method: Adopting a process-dynamics perspective, this study employs the T⁴ framework (Team Formation, Task and Role Development, Team Evolution, Team Optimization) and integrates systematic literature review (SLR) with cross-phase collaborative assessment modeling to achieve the first holistic, dynamic integration of HAT research across the entire lifecycle. Contribution/Results: We propose a “task–team bidirectional adaptation” analytical paradigm, uncovering core adaptive mechanisms—including role allocation, shared mental models, and backup behaviors. Six critical mechanisms influencing long-term collaborative efficacy are identified, and a comprehensive HAT adaptability assessment framework spanning the full lifecycle is established. This work provides both a theoretical foundation and an actionable roadmap for designing adaptive human–autonomy teams.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

The rapid advancement of AI, including Large Language Models, has propelled autonomous agents forward, accelerating the human-agent teaming (HAT) paradigm to leverage complementary strengths. However, HAT research remains fragmented, often focusing on isolated team development phases or specific challenges like trust calibration while overlooking the real-world need for adaptability. Addressing these gaps, a process dynamics perspective is adopted to systematically review HAT using the T$^4$ framework: Team Formation, Task and Role Development, Team Development, and Team Improvement. Each phase is examined in terms of its goals, actions, and evaluation metrics, emphasizing the co-evolution of task and team dynamics. Special focus is given to the second and third phases, highlighting key factors such as team roles, shared mental model, and backup behaviors. This holistic perspective identifies future research directions for advancing long-term adaptive HAT.

Problem

• Fragmented research on human-agent teaming phases and challenges
• Lack of adaptability in real-world human-agent teaming scenarios
• Need for systematic review using T^4 framework for HAT

Innovation

• Adopts process dynamics perspective for HAT
• Uses T$^4$ framework for systematic review
• Emphasizes co-evolution of task and team