Cognitive Distortions As Psychological Predictors Of Academic Procrastination Among University Students

Abstract

Academic procrastination is widely recognized as a pervasive self-regulatory problem in higher education and is consistently associated with lower performance, heightened distress, and impaired well-being. Contemporary evidence suggests that procrastination is not adequately explained by “poor time management” alone, but is closely linked to students’ cognitive appraisals of tasks and of the self. Within cognitive-behavioral frameworks, cognitive distortions—systematic errors in thinking such as catastrophizing, overgeneralization, selective abstraction, mind reading, and “should” statements—can intensify negative task-related emotions, undermine self-efficacy, and promote avoidance-based coping, thereby increasing the likelihood of academic delay. This article synthesizes research on the cognitive predictors of procrastination with a focus on cognitive distortions and related dysfunctional belief systems among university students. A narrative review approach was applied to peer-reviewed studies and seminal theoretical sources identified through searches of major databases and reference chaining. Findings converge on three propositions: maladaptive cognitions (cognitive distortions, irrational beliefs, dysfunctional attitudes) show reliable associations with procrastination indicators, although effect sizes vary by measurement and context; fear of failure, depressive symptoms, low self-esteem, and reduced academic self-efficacy frequently function as mediating pathways linking distorted cognitions to delay behavior; and interventions that target maladaptive appraisals (e.g., cognitive restructuring, appraisal inquiry, CBT-based skills training) demonstrate reductions in procrastination, supporting the plausibility of a cognitive mechanism. The review concludes with implications for assessment and university counseling and proposes priorities for future research, including longitudinal and experimental designs and more fine-grained measurement of distortion profiles across academic tasks.