Advancing Fairness in AI for Education: How UM6P Research Tackles Student Dropout Prediction with Ensemble Learning
Aboukacem Abdelghafour, PhD student at the UM6P College of Computing, has achieved another milestone with the acceptance of his second paper at the prestigious AIED 2025 Conference (Artificial Intelligence in Education), ranked A and to be held from July 22 to 26 in Palermo, Italy.
His research, titled “On the Fairness of Ensemble Learning Methods in Student Dropout Prediction”, is co-supervised by Prof. Ismail Berrada, Prof. Loubna Mekouar, Prof. Elhoucine Bergou, and Prof. Youssef Iraqi.
It explores the predictive power and fairness implications of ensemble learning techniques—such as bagging, boosting, voting, and stacking—when applied to educational dropout prediction tasks. This work contributes significantly to the field of AI in education, by addressing fairness in algorithmic decision-making—a topic of growing importance in designing equitable learning technologies.
Leave a Reply