A fairness-aware multi-objective integrated deep learning framework for intelligent tutoring systems
Year: 2026
Type: article
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.
Source: Scientific Reports
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7203922439
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