Dynamic Fairness-Adaptive Transfer Learning for Bias-Mitigated AI Personalized Learning Paths - Tan, Chaoying (International Journal of Advanced AI Applications, 2025)
We propose a dynamic fairness-adaptive transfer learning framework for personalized education that systematically addresses demographic biases while maintaining pedagogical efficacy. The proposed method integrates bias-aware Bayesian fairness analysis and fairness-constrained transfer learning into adaptive learning systems, enabling equitable personalization through hierarchical architecture. A bias quantification module employs hierarchical Bayesian modeling to estimate latent biases in historical educational data, isolating significant bias patterns that influence learning recommendations. The fairness-constrained transfer learning engine then adapts pre-trained models using a multi-task objective that jointly optimizes accuracy and demographic parity, dynamically adjusting the fairness-accuracy tradeoff via real-time feedback. Furthermore, the system introduces novel components such as a hierarchical variational autoencoder for disentangling pedagogical and bias factors, group-fair knowledge distillation for compressing large language models without propagating biases, and a differentiable sorting network for equitable resource allocation. Experimental validation demonstrates significant reductions in demographic disparities across multiple protected attributes while preserving or improving learning outcomes. The framework provides instructors with interpretable fairness-accuracy tradeoff metrics through a Shapley-value-based dashboard, facilitating transparent and actionable insights. This work advances the state-of-the-art in AI-driven education by formalizing a principled approach to bias mitigation that is both adaptive to individual learners and robust to demographic shifts. This article is a research result of 2025 project of the Sichuan Vocational College of Finance and Economics Collaborative Innovation Center for Financial Big Data, Research on “Research on the Design and Application of Multi-agent Introductory Assistant Based on Big Data Technology in Computer Science Specialty” (No. CSDSJ202509).