Behind the Screen: Examining the Relationship between Artificial Intelligence Dependency and Academic Procrastination among Undergraduate Students

Abstract

The relationship among undergraduate students' Artificial Intelligence (AI) dependency and academic procrastination is examined in this study. The cross-sectional survey design was used. The sample consisted of 300 undergraduate students (150 males and 150 females) attending colleges and University of Haripur. Purposive sampling technique was used to select the participants. The Generative Artificial Intelligence Dependency Scale (Goh, Hartanto, & Majeed, 2025) and the Tuckman Procrastination Scale (Tuckman, 1991) were used to measure AI dependency and academic procrastination, respectively. Pearson correlation analysis and an independent-samples t-test were performed to analyze the data. Results indicated there was no significant difference between the gender and dependency on AI in the undergraduate students. The results showed that there was a significant positive correlation between AI dependency and academic procrastination, suggesting that the higher the level of AI dependency, the more likely the students were to display procrastinatory behaviors. The results indicate that overusing AI tools could be linked to difficulties in academic task completion and potentially have a negative effect on academic self-regulation. The study concludes that it is essential to support the use of AI technologies in the context of higher education in responsible and balanced ways, while also keeping in mind the possible adverse academic impacts.