CTU-Tutor: Contextual and User-Aware Language Understanding for Personalized Tutoring with Long-Form Text Generation

Abstract

This study introduces CTU-Tutor, a contextual and user-aware intelligent tutoring system for generating personalized long-form text as learning content. The system combines BERT-based learner embeddings, K-means for proficiency clustering, Transformer-XL for long-context modelling, BiLSTM-CRF for concept extraction, and LDA-based knowledge graph construction. A retrieval-augmented Transformer-XL model produces personalized content, which is supplemented with explainable AI to make decisions transparently using SHAP and LIME. Experimental analysis shows that CTU-Tutor is better than state-of-the-art models, such as ExPerT, REST-PG, GSPT-CVAE, LONGLaMP, and Transformer_QA, in various metrics. The proposed framework has 0.98 accuracy, 0.98 F1-score, 0.97 MCC, and 0.98 precision, sensitivity, and specificity, and minimized false negatives (0.013) and false positives (0.017). For text generation, BLEU, ROUGE, and METEOR scores were approximately 0.98, indicating high overlap with the corresponding human-authored reference texts. These results prove that the synergistic implementation of learner profiling, long-context modelling, structured knowledge representation, and explainable AI delivers more precise, reliable, and pedagogically transparent personalized tutoring.