CURRICULUM-GUIDED META-REINFORCEMENT LEARNING FOR CROSS-DOMAIN TRANSFER IN INTELLIGENT EDUCATIONAL DIAGNOSTICS

The field of intelligent educational diagnostics faces significant challenges in adapting models across diverse learning domains and student populations. This research introduces a novel Curriculum-Guided Meta-Reinforcement Learning (CG-MRL) framework that integrates curriculum learning principles with meta-reinforcement learning (Meta-RL) to achieve effective cross-domain transfer in educational assessment systems. The proposed framework employs a hierarchical learning structure wherecurriculum sequences guide the meta-learning process, enabling adaptive knowledge transfer from source educational domains to target domains with minimal performance degradation. Through extensive experimentation across mathematics, language arts, and science education datasets, our CG-MRL approach demonstrates superior transfer efficiency compared to traditional reinforcement learning and standard meta-learning baselines. The framework achieves an average diagnostic accuracy improvement of 23.7% in cross-domain scenarios while reducing the required training samples by 41.3%. Furthermore, the curriculum-guided architecture enables interpretable learning trajectories that align with pedagogical theories, providing educators with actionable insights into student learning patterns. The convergence analysis reveals that curriculum-structured training achieves optimal performance with significantly fewer iterations across varying problem complexities. The results establish CG-MRL as a promising paradigm for building scalable and adaptable intelligent tutoring systems capable of serving heterogeneous educational contexts.