Accuracy Is Not Enough: A Cross-Architecture Audit of Demographic Bias in Deep Knowledge Tracing
Deep knowledge tracing (DKT) models implicitly decide which students an adaptive system believes have mastered a skill, yet almost all evidence on their demographic fairness comes from Bayesian knowledge tracing; the deep models that power modern systems have received no comparable cross-architecture audit. We close this gap: four architectures (DKT, DKVMN, SAKT, AKT) trained under three regimes (standard, reweighting, adversarial) on two public datasets with demographic metadata, Eedi (15.9M interactions) and OULAD (167k after preprocessing), evaluated with ABROCA, student-level bootstrap confidence intervals, and permutation tests addressing recent critiques of fairness-metric instabili...
| Authors | Dang Quang Minh, Nguyen Dung Son, Nguyen Huu Loi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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