Abstract: Text simplification plays a vital role in adaptive language learning, especially when aligned with the Common European Framework of Reference (CEFR) proficiency levels. The purpose of this study is to develop an interpretable and CEFR-aligned text simplification framework that produces pedagogically appropriate simplified texts for learners at different proficiency levels. Existing neural simplification approaches, such as ACCESS, MUSS, and EditNTS, primarily rely on single-level feedback or surface-level readability measures, limiting their ability to ensure both sentence-level linguistic simplicity and document-level coherence. To address these gaps, this study proposes CEFR-RefineNet, a hybrid framework integrating T5 for generative simplification and BERT for contextual CEFR-level classification, enhanced through a novel Dual-Level Explainable Feedback Loop (DL-EFL). The DL-EFL simultaneously evaluates sentence-level linguistic difficulty and document-level readability while providing token-level error attribution for interpretability. The model was implemented using Python and the Hugging Face Transformers library, trained and tested on the CEFR Levelled English Texts corpus comprising 1,500 texts spanning levels A1 to C2. Experimental results show that CEFR-RefineNet achieved a SARI score of 0.78, accuracy of 91%, and F1-score of 0.85, outperforming the strongest baseline (MUSS, 81% accuracy) by approximately 12%. The adaptive feedback mechanism accelerated reward convergence and improved CEFR compliance, ensuring more pedagogically suitable simplifications. In summary, the proposed CEFR-RefineNet establishes a transparent, interpretable, and performance-driven text simplification model capable of generating fluent, meaning-preserving, and CEFR-aligned texts, paving the way for intelligent and adaptive language-learning systems.

Pavani G, Myagmarsuren Orosoo, W. Grace Shanthi, Vinod Waiker, Aseel Smerat, Bhuvaneswari Pagidipati, Bansode G. S and Osama R. Shahin. “An Interpretable Dual-Level Feedback Approach for Improving Graded Language Simplification and Readability”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161170

@article{G2025,
title = {An Interpretable Dual-Level Feedback Approach for Improving Graded Language Simplification and Readability},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161170},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161170},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Pavani G and Myagmarsuren Orosoo and W. Grace Shanthi and Vinod Waiker and Aseel Smerat and Bhuvaneswari Pagidipati and Bansode G. S and Osama R. Shahin}
}