The Impact of AI-Assisted Personalized Learning on Student Achievement Gaps

Abstract

This paper examines the potential of artificial intelligence (AI)-assisted personalized learning systems to address persistent achievement gaps in education. Drawing on recent empirical studies and theoretical frameworks from educational technology and learning sciences, we investigate how AI-driven adaptive learning platforms can provide differentiated instruction that responds to individual student needs. The analysis reveals that while AI-assisted personalized learning demonstrates promising outcomes in improving academic performance across diverse student populations, its effectiveness depends significantly on implementation factors, including teacher training, technological infrastructure, and culturally responsive design. Results indicate that under optimal conditions, these systems can reduce achievement gaps by providing targeted support for traditionally underserved students, though they may introduce new forms of inequity when implemented without attention to existing structural disparities. This research contributes to understanding how educational technology can be leveraged to create more equitable learning environments while highlighting the importance of human oversight and context-sensitive implementation.