[論文レビュー] Unraveling Human-AI Teaming: A Review and Outlook

Bowen Lou, Tian Lu+2 more|ArXiv.org|Apr 8, 2025

Human-Automation Interaction and Safety被引用数 3

ひとこと要約

The paper analyzes human-AI teaming through an extended Team Situation Awareness framework and outlines a four-part research outlook on formulation, coordination, maintenance, and training for sustainable, high-performing human-AI teams.

ABSTRACT

Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity. Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities. This paper explores the evolution of AI agents from passive tools to active collaborators in human-AI teams, emphasizing their ability to learn, adapt, and operate autonomously in complex environments. This paradigm shifts challenges traditional team dynamics, requiring new interaction protocols, delegation strategies, and responsibility distribution frameworks. Drawing on Team Situation Awareness (SA) theory, we identify two critical gaps in current human-AI teaming research: the difficulty of aligning AI agents with human values and objectives, and the underutilization of AI's capabilities as genuine team members. Addressing these gaps, we propose a structured research outlook centered on four key aspects of human-AI teaming: formulation, coordination, maintenance, and training. Our framework highlights the importance of shared mental models, trust-building, conflict resolution, and skill adaptation for effective teaming. Furthermore, we discuss the unique challenges posed by varying team compositions, goals, and complexities. This paper provides a foundational agenda for future research and practical design of sustainable, high-performing human-AI teams.

研究の動機と目的

• Identify gaps in current human-AI teaming research related to aligning AI with human values and leveraging AI as genuine team members.
• Extend Team Situation Awareness theory to incorporate AI teammates and define an actionable research framework.
• Propose a four-dimensional agenda—formulation, coordination, maintenance, and training—for advancing human-AI teaming.
• Discuss challenges in trust, accountability, and adaptability within diverse team compositions and contexts.

提案手法

• Synthesize and extend Team Situation Awareness (SA) theory to include AI roles and dynamic coordination.
• Define an extended model of Team SA for human-AI teaming with emphasis on role specification and role fluidity.
• Map existing literature on human perception, comprehension, and projection SA to AI-enabled teams.
• Provide a conceptual roadmap and future research directions grounded in team-learning and social dynamics.

実験結果

リサーチクエスチョン

• RQ1 How can Team SA be extended to accommodate agentic AI as team members?
• RQ2 What are the key challenges and mechanisms for aligning AI with human values and objectives in teams?
• RQ3 How do formulation, coordination, maintenance, and training influence the effectiveness of human-AI teaming?
• RQ4 What design principles improve trust, accountability, and shared mental models in human-AI teams?

主な発見

• AI agents are evolving into autonomous, iterative, and intelligent team members capable of learning and adapting in complex environments.
• Two major gaps are identified: aligning AI with human values and utilizing AI capabilities as genuine team members.
• An extended Team SA model is proposed with emphasis on shared mental models, role specification, and role fluidity to support dynamic collaboration.
• The paper outlines a structured four-dimensional research outlook: team formulation, coordination, maintenance, and training, addressing trust, accountability, and long-term sustainability.