The increasing integration of artificial intelligence and algorithmic systems in educational settings has raised critical concerns about their impact on educational equity. This paper examines the manifestation and implications of algorithmic bias across various educational domains, including admissions processes, assessment systems, and learning management platforms. Through analysis of current research and studies, we investigate how these biases can perpetuate or exacerbate existing educational disparities, particularly affecting students from marginalized communities. The study reveals that algorithmic bias in education operates through multiple channels, from data collection and algorithm design to implementation practices and institutional policies. Our findings indicate that biased algorithms can significantly impact students' educational trajectories, creating new forms of systemic barriers in education. We propose a comprehensive framework for addressing these challenges, combining technical solutions with policy reforms and institutional guidelines. This research contributes to the growing discourse on ethical AI in education and provides practical strategies for creating more equitable educational systems in an increasingly digitized world.

Citation Key: obedboateng2025