Abstract: The integration of Artificial Intelligence (AI) into English as a Foreign Language (EFL) education offers new opportunities for developing adaptive and engaging learning materials. Narrative-based content is central to improving reading comprehension, vocabulary acquisition, and learner motivation. However, maintaining grade-appropriate readability in AI-generated narratives remains a major challenge. This study presents Readability-Driven Prompting (RDP), a novel technique designed to enhance the accuracy and efficiency of large language models in generating grade-level narratives. Using GPT-4o-mini, three prompting strategies—CEFR Keyword-Constrained Prompting (CKCP), Instruction-Based Prompting (IBP), and the proposed RDP—were applied to produce narratives for 7th-grade (A1–A2 CEFR) and 10th-grade (B1–B2 CEFR) learners. The outputs were evaluated using Flesch Reading Ease (FRE), Dale–Chall (DC) readability metrics, lexical analysis, and human assessments. Experimental results indicate that the RDP approach achieves higher alignment with target readability levels and improved lexical appropriateness compared to baseline methods, demonstrating a scalable and effective strategy for generating educational narratives, particularly for beginner-level learners.

Ronald William Marbun and Makoto Shishido. “A Readability-Driven Prompting Framework for Accurate Grade-Specific EFL Narrative Creation”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170109

@article{Marbun2026,
title = {A Readability-Driven Prompting Framework for Accurate Grade-Specific EFL Narrative Creation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170109},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170109},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Ronald William Marbun and Makoto Shishido}
}