Educational Data Mining (EDM) offers great potential for personalized learning, predicting student performance, improving curriculum and instruction, and enhancing student engagement through data-driven insights. However, it faces challenges like data quality issues, privacy and ethical concerns, algorithmic bias, the need for interdisciplinary expertise, and ensuring transparency and interpretability of the models it creates.