Abstract

This study presents an adaptive English text regeneration system that modifies authentic materials to match learners' CEFR (Common European Framework of Reference for Languages) proficiency levels (A1–C2). It addresses the crucial challenge of accessibility while maintaining the original meaning. Leveraging advancements in large language models (LLMs), our framework employs a three-phase process: first, CEFR-based text analysis utilizing curated vocabulary lists and syntactic metrics; second, multi-level regeneration through the fine-tuned Qwen 2.5 model; and third, rigorous validation of semantic fidelity (achieving a 92% BERT score) and readability. Experimental results with 300 learners indicate significant improvements, with a 32% increase in comprehension for beginner groups and a 25% increase for intermediate groups. Additionally, there is a 40% decrease in self-reported anxiety. The system's real-time processing capability (under 3 seconds per page) ensures practical scalability.Our work makes three key contributions: it establishes the first comprehensive framework covering all six CEFR levels with empirical validation; it integrates pedagogical and psychological principles to boost learner motivation and reduce anxiety; and it demonstrates the effectiveness of progressive complexity scaffolding while setting actionable benchmarks for LLM-driven educational tools. By balancing linguistic precision with psychological benefits—such as increased motivation and confidence—the system enhances the role of AI in language education. Future research will focus on adapting colloquial language and examining longitudinal impacts on knowledge retention, further bridging the gap between authentic content and learner needs.