The Impact of Artificial Intelligence based Personalized Learning on Students Motivation and Self-Regulated Learning

Abstract

This quantitative study examined the impact of Artificial Intelligence (AI)–based personalized learning on students’ motivation and self-regulated learning in higher education. Using a descriptive research design, data were collected from 120 students through a structured questionnaire employing a Likert-scale format. Descriptive statistics, correlation, regression, and ANOVA analyses were used to analyze the data. The findings revealed a significant positive relationship between AI-based personalized learning and both student motivation and self-regulated learning, indicating that adaptive AI tools enhance learners’ engagement, autonomy, and strategic learning behaviors. Regression results confirmed that AI-based personalized learning was a strong predictor of motivation and self-regulated learning, while ANOVA findings showed significant differences based on academic level, with postgraduate students demonstrating higher levels of motivation and self-regulation than undergraduates. Despite these positive outcomes, challenges such as digital literacy gaps, ethical concerns, and equitable access were identified. Overall, the study highlights the potential of AI-based personalized learning to foster motivated, autonomous, and self-regulated learners, while emphasizing the need for thoughtful and ethical implementation in higher education settings.