Presentation
33. Evaluating Cross-Training's Impact on Perceived Teaming Outcomes for Human-AI Teams
SessionPoster Session 2
DescriptionThe 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 the cross-training type, positional modeling, 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.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
Aging
Augmented Cognition
Children's Issues
Communications
Cybersecurity
Education
Environmental Design
General Sessions
Human AI Robot Teaming (AI)
Macroergonomics
Occupational Ergonomics
Student Forum
Surface Transportation
Sustainability
System Development