Team Dynamics in Human-AI Collaboration: Effects on Confidence, Satisfaction, and Accountability

Abstract

The integration of multimodal AI agents into human teams raises critical questions about collaboration in ad hoc environments without pre-coordination. We examine how team composition affects psychological dimensions in Human-AI Teaming by investigating self-confidence, satisfaction, and accountability across configurations. Using a factorial design, we compared four conditions (Human-Only, Human-Human, Human-Agent, Human-Human-Agent) across resource management, healthcare, and finance domains. Fifty-four participants completed decision tasks using a tree-of-thought framework, with performance assessment and psychological measures. Results revealed domain-specific patterns: participants preferred human-led teams for health decisions but AI-assisted teams for data-driven tasks. Satisfaction did not increase when incorporating agents into human-expert teams, suggesting cognitive load constraints. Accountability attribution varied with perceived performance, with users taking more responsibility for failures while distributing credit for successes. These findings, grounded in cognitive load theory, advance our understanding of psychological dimensions in ad hoc teaming and provide a framework for designing domain-appropriate HAT systems that balance performance with ethical considerations.