Unraveling Human-AI Teaming: A Review and Outlook

• URL: http://arxiv.org/abs/2504.05755v2
• Date: Wed, 09 Apr 2025 12:20:05 GMT
• Title: Unraveling Human-AI Teaming: A Review and Outlook
• Authors: Bowen Lou, Tian Lu, T. S. Raghu, Yingjie Zhang,
• Abstract summary: Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity.<n>Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities.<n>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.
• Score: 2.3396455015352258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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