Academic procrastination and achievement among university students under AI overreliance and contextual factors

Abstract

Academic procrastination remains a persistent challenge in higher education, particularly as artificial intelligence (AI) tools become increasingly integrated into students’ learning activities. While AI can assist with academic tasks, excessive reliance on such technologies may influence students’ learning behaviors and time management. This study employed a quantitative, cross-sectional design to examine the factors associated with academic procrastination and its relationship with academic achievement in higher education contexts where AI tools are widely used. Data were collected from 301 university students and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that the model explains a substantial proportion of variance in academic procrastination (R²=0.72) and a moderate proportion in academic achievement (R²=0.30). Academic procrastination shows a significant relationship with academic achievement (β=0.551, p<0.001). Among the predictors, self-regulated learning (β=-0.800) and school attachment (β=-0.803) exhibit the strongest negative associations with procrastination, while AI overreliance (β=0.430) increases academic procrastination. Digital competence (β=-0.441) also contributes to reducing procrastination. These findings suggest that academic procrastination functions as a key mechanism linking individual, contextual, and technological factors to academic outcomes in technology-supported higher education. The results highlight the importance of strengthening students’ self-regulated learning and promoting cautious and responsible AI use in higher education.