Task-Technology Fit and the Productive Use of Generative Artificial Intelligence in Student Global Virtual Teams

This study explores how the use of generative artificial intelligence (GAI) in a collaborative, global virtual team-based international business project relates to team performance and individual peer evaluations. Addressing ongoing debates regarding the extent to which GAI improves learning outcomes, the study applies task–technology fit (TTF) theory to investigate which specific GAI use cases enhance performance in collaborative academic work. Using a large international sample (N = 3193) of students working in real global virtual consulting teams for real client organizations, we examine how different GAI applications relate to team report grades and peer assessments. The findings reveal that using GAI for proofreading and learning is positively associated with both team and individual performance. In contrast, recreational use during project work does not contribute to project outcomes and is negatively associated with performance. By extending task–technology fit theory to a team-based, global, and performance-oriented educational context, the study demonstrates that simply using GAI does not guarantee better results. Instead, performance gains depend on aligning GAI use with task requirements and sound academic practice. The findings underscore the importance of pedagogical guidance that helps students learn when and for what purposes GAI can be used effectively in experiential, team-based learning.