AI-DRIVEN ADAPTIVE LEARNING’S EFFECT ON STUDENT MOTIVATION AND SELF-EFFICACY IN PUBLIC SECTOR UNIVERSITIES

Abstract

The integration of artificial intelligence (AI) in higher education has introduced new opportunities for personalized and adaptive learning. This study investigated the effects of AI-driven adaptive learning systems on student motivation and academic self-efficacy in public-sector universities in Pakistan. A quantitative research design was employed, and data were collected through a structured questionnaire administered to 200 students from four public universities in Lahore. The survey assessed students’ exposure to AI-based adaptive learning tools, their motivation, and their academic self-efficacy. Descriptive and inferential statistical techniques, including correlation and regression analyses and independent-samples t-tests, were used to analyze the data. The findings revealed a significant positive relationship between AI-driven adaptive learning and both student motivation and academic self-efficacy. Regression results indicated that AI-based adaptive learning was a strong predictor of motivation and self-efficacy, accounting for a substantial proportion of the variance in student outcomes. Additionally, students with higher levels of AI use demonstrated significantly greater motivation and self-efficacy than those with lower levels of AI use.