Artificial Intelligence–Supported Personalized Learning and Its Impact on Academic Engagement Among Students with Special Educational Needs in Inclusive Classrooms

Abstract

Artificial intelligence (AI)-supported personalized learning has emerged as a promising approach for addressing the diverse educational needs of students with special educational needs (SEN) in inclusive classrooms. This study examined the impact of AI-supported personalized learning on academic engagement and related educational outcomes by comparing students receiving AI-assisted instruction with those receiving conventional teaching methods. A quantitative comparative research design was employed using data from 300 students, equally distributed between AI-supported and conventional instructional groups. Independent and paired sample t-tests, Pearson correlation analysis, reliability analysis, and multiple linear regression were conducted to evaluate group differences, relationships among variables, and predictors of academic engagement. The findings revealed that students exposed to AI-supported personalized learning demonstrated significantly higher post-intervention academic engagement, academic performance, attendance, task completion, and student satisfaction than those receiving conventional instruction (p < 0.001). Regression analysis further identified personalization, AI usage duration, teacher support, and attendance as significant predictors of post-intervention engagement, while accessibility and feedback quality were not significant independent predictors after adjustment. Overall, the findings indicate that AI-supported personalized learning can substantially enhance inclusive educational practices by promoting individualized learning experiences and improving student participation and achievement. The study highlights the importance of integrating adaptive AI technologies with effective teacher support to create equitable, learner-centered, and inclusive educational environments.