In 2026, educational institutions rely on data to drive student success. From predictive enrollment models to personalized learning paths, algorithms now influence critical academic outcomes. However, a significant technical hurdle remains: algorithmic bias. When left unchecked, biased models reinforce systemic inequities, often disadvantaging students based on race, gender, or socioeconomic...