Explainable Deep Learning Framework for Recommendation of Ideological and Political Curriculum Resources

The accelerated digitalization of education systems has enormously improved the supply of political and ideological curriculum material. Nonetheless, traditional recommendation models, usually premised on collaborative filtering and simple content-based strategies, are plagued by limited personalization, weak scalability, and transparency in decision-making. These shortcomings discourage teachers and students from effectively recognizing quality, contextually appropriate materials. Current deep learning models, are better improved recommendation accuracy, are still in need of explainable mechanisms to justify recommendations within an educational scenario and hence lower trust and adoption. In order to solve these problems, this research suggests an Explainable Deep Learning Framework using Transformerimproved Neural Collaborative Filtering (T-NCF) combined with attention-based interpretability and SHAP analysis. The approach takes advantage of multi-modal input features learner profiles, usage history, and metadata about the resources and supports robust personalization along with providing explicit, human-understandable justifications for every recommendation. Experimental evaluation on a hand-curated set of ideological and political education materials shows that the system proposed in this work has an accuracy of 98.9 %, well outperforming state-of-the-art baseline models. This method not only enhances the accuracy of recommendations but also increases transparency, which strengthens the trust between learners and educators.