AI-Based Personalized Education: Exploring the Psychological Determinants of Student Achievement and Persistence

Abstract

This is a quantitative study that investigated the connection between AI-based personalized education and student achievement and persistence and the mediating variable of psychological factors, including self-efficacy, motivation, engagement, and self-regulated learning. One hundred and eighty college educators spread throughout Pakistan were included in the study using a self-administered Likert-based questionnaire. The results showed that there is a positive significant relationship between AI-based personalization and academic performance, which was expressed in regression analysis, with AI personalization accounting for 38.4% of the difference in academic performance. In addition, the findings of the multiple regression revealed that psychological predictors all together explained 61 percent of the variation in student achievement and persistence with self-efficacy being the most important predictor. The outcomes of ANOVA proved that the relation between the personalized education based on AI and persistence significantly depended on the psychological factors, implying that these internal mechanisms increase the persistence in study among students. The authors concluded that AI-dendrodate personalization improves the learning results, but its impact is the most effective in the case of a combination with psychological support mechanisms. It is suggested that schools and colleges create the AI systems that facilitate motivation, self-confidence, and self-directed learning in order to stimulate the long-term academic success.