FairMORL: A Framework for Synergistic Optimization of Mastery, Engagement, and Equity in Adaptive Tutoring

Abstract Intelligent tutoring systems (ITS) increasingly rely on reinforcement learning (RL) to personalize learning pathways. However, existing approaches often optimize for a single objective of student mastery, neglecting the critical roles of student engagement and fairness across diverse learner populations. This narrow focus risks creating systems that are not only suboptimal from a pedagogical standpoint but also have the potential to exacerbate existing educational inequities. We introduce FairMORL, a comprehensive multi-objective reinforcement learning framework designed to address the complex trilemma of jointly optimizing for student mastery, engagement, and equity. The framework embeds fairness not as a post-hoc constraint but as a first-class objective within the learning process. Evaluated on the large-scale EdNet-KT1 dataset, comprising 1.2 million interactions from over 800 students, FairMORL demonstrates a statistically significant 12.3% improvement in mastery () and a 41% reduction in fairness violations, as measured by statistical parity difference, when compared to strong, mastery focused baselines. This work introduces five novel contributions that form an integrated toolkit for developing and auditing equitable ITS: (1) a counterfactual fairness analysis to quantify opportunity loss due to biased policies; (2) a method for monitoring temporal fairness drift to ensure equity in dynamic, long-term deployments; (3) the application of Integrated Gradients for policy interpretability, enhancing transparency and trust; (4) skill exposure heatmaps as a diagnostic tool for content-level bias; and (5) an analysis of Pareto-optimal frontiers to empower stakeholders to navigate utility-fairness trade-offs explicitly. Our results challenge the conventional wisdom of a direct trade-off between fairness and performance in educational contexts, suggesting instead that these goals can be synergistic. We provide a reproducible, open-source pipeline to facilitate future research and development of ITS that are not only effective and engaging but also fundamentally equitable.