Personalized learning has gained attention in English as a Foreign Language (EFL) education, where engagement and motivation play crucial roles in reading comprehension. We propose a novel approach to generating personalized English reading comprehension tests tailored to students' interests. We develop a structured content transcreation pipeline using OpenAI's gpt-4o, where we start with the RACE-C dataset, and generate new passages and multiple-choice reading comprehension questions that are linguistically similar to the original passages but semantically aligned with individual learners' interests. Our methodology integrates topic extraction, question classification based on Bloom's taxonomy, linguistic feature analysis, and content transcreation to enhance student engagement. We conduct a controlled experiment with EFL learners in South Korea to examine the impact of interest-aligned reading materials on comprehension and motivation. Our results show students learning with personalized reading passages demonstrate improved comprehension and motivation retention compared to those learning with non-personalized materials.

翻译：在英语作为外语（EFL）教育中，个性化学习已受到关注，其中参与度和动机在阅读理解中起着关键作用。我们提出了一种新颖的方法，用于生成根据学生兴趣定制的个性化英语阅读理解测试。我们利用OpenAI的gpt-4o开发了一个结构化的内容重构流程：从RACE-C数据集出发，生成在语言上与原始篇章相似、但在语义上与个体学习者兴趣对齐的新篇章及多项选择阅读理解问题。我们的方法整合了主题提取、基于布鲁姆分类法的问题分类、语言特征分析和内容重构，以增强学生的参与度。我们在韩国EFL学习者中进行了对照实验，以研究兴趣对齐阅读材料对理解和动机的影响。结果表明，与使用非个性化材料学习的学生相比，使用个性化阅读篇章学习的学生在理解和动机保持方面表现出显著提升。