Drivers and academic outcomes of generative AI use among university students in Benin

Generative Artificial Intelligence (GenAI) is increasingly being integrated into higher education, offering potential gains in efficiency and quality, yet there are concerns about its cognitive and behavioral consequences. This study surveyed 633 university students in Benin to examine factors associated with GenAI use and its academic, behavioral and psychological outcomes. Drawing on the Expectancy Value Theory and the Unified Theory of Acceptance and Use of Technology (UTAUT), we used Partial Least Squares Structural Equation Modeling to assess how Academic Workload, Time Pressure, and motivational sensitivities relate to GenAI use and its association with Academic Performance, Procrastination, and Self-Perceived Memory Decline. Results show that Academic Workload, Time Pressure, and motivational factors (Sensitivity to Rewards and Quality) were positively associated with GenAI use, which was widespread – more than 90% of students reporting use at least twice per week. GenAI use was positively associated with higher Academic Performance, but also with increased Procrastination and Self-Perceived Memory Decline. Contrary to prior research, age, gender, and education level were not significantly associated with GenAI use, reflecting the widespread GenAI adoption across student groups. These findings highlight GenAI’s dual role as both a potential performance-support tool and a mechanism of cognitive offloading, underscoring the need for balanced integration strategies that leverage AI-assisted efficiency while preserving meaningful cognitive engagement and mitigating risks associated with overreliance on AI tools.