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Unraveling Human-AI Teaming: A Review and Outlook

Bowen Lou, Tian Lu, T. S. Raghu, Yingjie Zhang

TL;DR

The paper tackles the gap between rapid AI advancement and effective human-AI teamwork. It advances an extended Team Situation Awareness framework to model how humans and agentic AI can form shared mental models, coordinate, maintain trust, and learn. It identifies two critical gaps—alignment of AI with human values and full integration of AI as team members—and proposes a four-part research agenda: formulation, coordination, maintenance, and training. The work provides a foundational roadmap for designing sustainable, high-performing human-AI teams with implications for theory, design, and governance.

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.

Unraveling Human-AI Teaming: A Review and Outlook

TL;DR

The paper tackles the gap between rapid AI advancement and effective human-AI teamwork. It advances an extended Team Situation Awareness framework to model how humans and agentic AI can form shared mental models, coordinate, maintain trust, and learn. It identifies two critical gaps—alignment of AI with human values and full integration of AI as team members—and proposes a four-part research agenda: formulation, coordination, maintenance, and training. The work provides a foundational roadmap for designing sustainable, high-performing human-AI teams with implications for theory, design, and governance.

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.

Paper Structure

This paper contains 30 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Revisiting Human-AI Teaming
  3. Emerging Agentic AI Systems
  4. Definition of Human–AI Teaming
  5. Theoretical Framework: An Extended Model of Team Situation Awareness
  6. Mapping Existing Works to Human-AI Teaming
  7. Human Perception SA of Intelligent Agent
  8. Human Comprehension SA of Intelligent Agent
  9. Tasks in Human-AI Collaboration
  10. Human-human Teaming
  11. Outlook for Future Research in a New Era of AI Renaissance
  12. Understanding Human Learning: Human Projection of AI Agents
  13. Expected Outcomes
  14. Evaluations and Issues
  15. Mechanisms and Resolutions
  16. ...and 15 more sections

Figures (4)

  • Figure 1: An Extended Model of Team SA in Human–AI Teaming
  • Figure 2: Summary of Future Research Directions
  • Figure 3: A Framework for Team Formulation and Coordination in Human–AI Teams
  • Figure 4: A Framework for Team Maintenance and Evolution in Human–AI Teams