Unraveling Human-AI Teaming: A Review and Outlook

TL;DR

This paper reviews the evolution of human-AI teaming, identifies key research gaps, and proposes a structured framework emphasizing shared mental models, trust, and adaptability for effective collaboration.

Contribution

It offers a comprehensive review of human-AI teaming, highlights critical gaps, and proposes a new research framework focusing on formulation, coordination, maintenance, and training.

Findings

Identifies two major gaps: AI alignment with human values and underutilization of AI capabilities.

Proposes a structured research outlook with four key aspects of human-AI teaming.

Highlights importance of shared mental models and trust in effective collaboration.

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.