Language learners should regularly engage in reading challenging materials as part of their study routine. Nevertheless, constantly referring to dictionaries is time-consuming and distracting. This paper presents a novel gaze-driven sentence simplification system designed to enhance reading comprehension while maintaining their focus on the content. Our system incorporates machine learning models tailored to individual learners, combining eye gaze features and linguistic features to assess sentence comprehension. When the system identifies comprehension difficulties, it provides simplified versions by replacing complex vocabulary and grammar with simpler alternatives via GPT-3.5. We conducted an experiment with 19 English learners, collecting data on their eye movements while reading English text. The results demonstrated that our system is capable of accurately estimating sentence-level comprehension. Additionally, we found that GPT-3.5 simplification improved readability in terms of traditional readability metrics and individual word difficulty, paraphrasing across different linguistic levels.

翻译：语言学习者应在日常学习中定期阅读具有挑战性的材料。然而，频繁查阅词典既耗时又易分散注意力。本文提出一种新型注视驱动句子简化系统，旨在提升阅读理解能力的同时保持学习者对内容的专注。该系统整合了针对个体学习者定制的机器学习模型，通过结合眼动特征与语言特征评估句子理解程度。当系统检测到理解困难时，借助GPT-3.5将复杂词汇与语法替换为更简单的表达，提供简化版本。我们针对19名英语学习者开展实验，收集其阅读英文文本时的眼动数据。结果表明，该系统能够准确估计句子层面的理解水平。此外，我们发现GPT-3.5的简化处理在传统可读性指标与个体词汇难度方面均提升了可读性，并在不同语言层面实现了同义改写。