Fair AI in Educational Predictions: A Multi-Group Fairness Approach Using Reinforcement Learning

Machine learning (ML) has become integral to online education, enhancing prediction, personalization, and automated assessment. However, algorithmic bias remains a critical barrier to equitable learning, as ML models can systematically under- or over-estimate outcomes for particular demographic groups. Existing fairness approaches—especially those focused on single attributes such as race or gender—fail to capture the complex, intersectional identities that shape students' experiences. Even multi-group fairness methods face key limitations in educational contexts, including computational scalability and difficulty adapting to shifting data distributions and fairness priorities. To address these challenges, this study proposes a reinforcement learning (RL)-based pre-processing framework that dynamically reweights data to optimize both predictive accuracy and multi-group fairness while safeguarding privacy. An AUC-based fairness metric ensures stability as subgroup combinations increase, and explainable AI (XAI) techniques enhance interpretability. Using large-scale data from Algebra Nation and state assessments, results show that the proposed framework achieves improved fairness and stable accuracy, offering a scalable, model-agnostic, and privacy-preserving pathway toward trustworthy AI in education.