One-Topic-Doesn't-Fit-All: Transcreating Reading Comprehension Test for Personalized Learning

This paper develops and evaluates an LLM-powered pipeline (using GPT-4o) that transcreates English reading comprehension passages and questions to align with individual EFL students' interests, maintaining linguistic complexity while adapting semantic content. A controlled experiment with South Korean EFL learners demonstrates that personalized, interest-aligned reading materials improve both comprehension and motivation retention compared to non-personalized materials.

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 li

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