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