Fairness-Constrained Curriculum Adaptation for AI-Enhanced Education: A Multi-Objective Optimization Framework with NLP-Driven Cultural Analysis

We propose a fairness-constrained curriculum adaptation framework for AI-enhanced education that integrates natural language processing (NLP) with multi-objective optimization to dynamically tailor learning materials while ensuring cultural responsiveness and ethical alignment. The proposed method employs a Transformer-based language model to analyze student-generated text, extracting latent cultural and demographic features, which are then clustered using a fairness-sensitive algorithm to mitigate biased representations. These features are combined with conventional performance metrics to formulate a multi-objective optimization problem that simultaneously maximizes pedagogical effectiveness and minimizes disparities across demographic groups. The optimization framework incorporates differentiable fairness constraints, such as equalized odds, to ensure equitable learning outcomes while adhering to institutional guidelines. Moreover, the system interfaces with existing educational platforms through input and output substitution mechanisms, enabling seamless integration with conventional modules. A key novelty lies in the coupling of NLP-driven cultural analysis with constrained optimization, which provides a transparent and auditable approach to curriculum adaptation. Furthermore, the framework includes a human-in-the-loop component, allowing educators to override recommendations and refine the system iteratively. Experimental validation demonstrates that the method achieves superior fairness-performance trade-offs compared to baseline approaches, offering a scalable solution for equitable education. The work contributes to the growing discourse on ethical AI in education by addressing both technical and societal challenges through a mathematically rigorous yet interpretable framework.