A fairness-aware multi-objective integrated deep learning framework for intelligent tutoring systems

Abstract

The advancements of modern artificial intelligence in education (AIEd) systems have greatly improved prediction accuracy and personal pacing. Traditional intelligent tutoring systems have the highest achievable aggregate predictive accuracy, a value that often is rooted in historical biases, mis-represents engagement signals from advantaged learner groups, and leaves vulnerable learner groups out of the picture. To address these challenges, we develop a mathematically robust framework of deep optimization with multiple objectives to ensure equity is maintained and carried forward across the tutoring lifecycle. We propose 5 main components: (i) demographic sensitivity gradient encoding (DSGE) for measures and limits direct demographic influence by computing gradient-level sensitivities of the learning loss with respect to latent demographic embeddings; (ii) counterfactual equity replay networks (CERN) which guided by the DSGE signal, CERN models the learning process through an explicit structural causal model and uses offline counterfactual simulation to quantify fairness-sensitive trajectory differences under stated identification assumptions. (iii) The engage-weighted fairness attention fusion dynamically balances student persistence and fairness risk, in order to not let high-level engagements mask concerns for fairness. (iv) Pareto-adaptive equity-accuracy-engagement optimizer adapts objective weights on the simplex space through a meta-gradient optimization which promotes stable convergence across the competing objectives under the adopted training procedure. In (v) equity-preserving policy distillation and validation, the high-capacity multi-objective model is compressed to a light-weight student model while ensuring that the student model preserves the equity of the multi-objective model when deployed. Our framework is validated using three real-world, publicly available educational datasets viz., EdNet, ASSISTments, and open university learning analytics dataset. Empirical results indicate that our framework achieves up to 67% lower learning gain disparity and 56% greater stability of dispersion in learning-gap distributions, while maintaining learning accuracy within 1.2% of the unconstrained, accuracy-only baselines.