Artificial Intelligence Tools and Academic Performance: The Mediating Role of Self-Regulated Learning

The present study aimed to examine the mediating role of self-regulated learning in the relationship between the use of artificial intelligence (AI) tools and the academic performance of undergraduate university students in Faisalabad, Pakistan. A quantitative cross-sectional research design was employed. The study sample comprised 250 students from public and private universities who were selected using a stratified sampling technique. AI tool usage, self-regulated learning, and academic performance were assessed using a structured questionnaire consisting of validated scales. Demographic information, including age, gender, academic discipline, and semester, was also collected. Data were analyzed using SPSS and SmartPLS. Descriptive statistics and demographic analysis were used to summarize the characteristics of the sample, while Cronbach's alpha was used to assess the internal consistency reliability of the measurement scales. Correlation analysis was conducted to examine the associations among the study variables. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to examine the direct effect of AI tool usage on academic performance, while bootstrapping procedures were used to test the significance of the mediating effect. The findings supported a significant direct relationship between AI tool usage and academic performance, as well as a significant partial indirect relationship mediated by students' self-regulated learning. The results demonstrated that the use of AI tools had a significant positive effect on academic performance. These findings provide valuable insights for higher education institutions seeking to integrate AI tools to enhance student learning and academic success.