Epistemic Justice-Aware Checklist Learning an Adaptive and Auditable Governance Framework for Bias and Fairness in Generative AI Curriculum Design

Generative AI increasingly participates in curriculum design, yet the resulting syllabi, learning outcomes, and assessments may reproduce subtle epistemic injustices that existing governance instruments fail to detect. We propose an adaptive and auditable framework that learns a bias and fairness checklist for generative AI curriculum artifacts rather than treating such a checklist as a static set of rules. The proposed method encodes curriculum texts into two disentangled latent subspaces, one capturing pedagogical utility and the other capturing epistemic justice violations, through a contrastive objective combined with intersectional adversarial debiasing. A psychometric utility-preserving penalty constrains this separation so that debiasing does not degrade instructional quality. Each checklist item is then evaluated by an evidential deep learning head that outputs a Dirichlet distribution over compliance states, hence yielding calibrated violation probabilities and explicit uncertainty estimates. An uncertainty-aware routing mechanism subsequently directs low-confidence drafts to a Faculty Expert Panel, whose decisions and justification notes are returned to a fine-tuning dataset. This feedback loop updates the checklist definitions and model weights iteratively, therefore allowing the governance instrument to adapt to newly identified forms of subtle bias. The main contribution is a mathematically grounded pipeline that couples representation disentanglement, evidential uncertainty quantification, and human expert feedback within a single auditable process. For curriculum governance, the framework offers transparent violation probabilities, traceable expert interventions, and a documented evolution of fairness criteria, which together support accountability in generative AI-assisted educational design.