AI-teaming: Redefining collaboration in the digital era

Area of Science:

• Human-AI Interaction
• Team Dynamics
• Artificial Intelligence

Background:

• Human-AI teams (HATs) are an emerging area with significant potential.
• Understanding team dynamics, trust, and cognition in HATs is crucial for effective integration.
• Current HATs frequently underperform despite AI's capabilities.

Purpose of the Study:

• To examine team constellations and dynamics in HATs.
• To analyze trust in artificial intelligence (AI) teammates.
• To investigate shared cognition within HATs.

Main Methods:

• Literature review and synthesis of existing research on human-AI teaming.
• Analysis of factors influencing team performance in HATs.
• Examination of trust dynamics and shared cognition models.

Main Results:

• Integrating AI teammates can initially decrease coordination, communication, and trust.
• Trust in AI often declines over time due to overestimation of capabilities, hindering teamwork.
• HATs frequently exhibit poor team cognition and lack of mutual understanding, leading to underperformance.

Conclusions:

• Realizing the full potential of HATs requires addressing challenges in team cognition and mutual understanding.
• Interdisciplinary collaboration between computer science and psychology is essential for future research.
• Development of robust theoretical frameworks is needed to guide the design and implementation of effective HATs.