ALGORITHMIC BIAS IN AI-DRIVEN EDUCATIONAL MANAGEMENT SYSTEMS: IMPLICATIONS FOR DECISION-MAKING IN EDUCATIONAL INSTITUTIONS
Abstract
Artificial intelligence (AI) technologies are increasingly integrated into educational management systems to support administrative decision-making, automate assessment processes, and predict student outcomes. While these systems promise efficiency and data-driven governance, they also introduce significant risks related to algorithmic bias. Algorithmic bias occurs when AI systems produce systematic and unfair outcomes due to biased training data, incomplete contextual information, or flawed algorithmic design. In educational environments, such bias may influence decisions regarding grading, student performance prediction, and institutional resource allocation. This article examines algorithmic bias in AI-driven educational management systems and its implications for decision-making in educational institutions.
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