Strategies For Reducing Educational Inequality In Primary Schools Using Adaptive Learning Technologies

Abstract

Primary schools still wrestle with systemic inequality that disproportionately affects lower-income students. AI-driven learning tools, often called Adaptive Learning Technologies or ALTs, offer tailored lessons that can improve learning outcomes for students. This article dives into ways these systems can reduce inequalities by improving academic performance, motivation, and access. Research generally shows that ALTs can deliver learning outcomes much like one-on-one tutoring, enhancing both retention and engagement. Yet, challenges such as unequal access, poor teacher training, and ethical concerns such as data privacy and algorithmic bias tend to hold implementation back. Getting these tools off the ground depends largely on strong institutional support, curriculum improvement and active teacher involvement. This article provides the evidence that ALTs have a positive impact on student outcomes while underscoring the need for long-term, sustainable approaches and clear oversight to ensure fairness. Future research should investigate how these methods impact motivation over time and the flexibility of the technology as situations shift.