Evaluating Cross-Training’s Impact on Perceived Teaming Outcomes for Human-AI Teams

Abstract

The rapid integration of artificial intelligence (AI) across various industries has given rise to human-AI teams (HATs), where collaboration between humans and AI may leverage their unique strengths. However, these teams often face performance challenges due to mismatches between human expectations and AI capabilities, hindering the effectiveness of these future workforce teams. Addressing these discrepancies, team training, particularly cross-training, has emerged as a promising intervention to align expectations and enhance team dynamics. This study explores the efficacy of different cross-training approaches and human/AI team role assignments on team training reactions and perceived task performance in an advertising co-creation task. The findings suggest that cross-training significantly improves both training reactions and task performance perceptions. By extending traditional team training methods to HATs, this research suggests that cross-training may serve as a viable strategy to improve team effectiveness and support the future workforce.