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

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Jieun Han , Daniel J. Lee , Haneul Yoo , Jun‐Hyoung Park , Soyeon Ahn , Alice Oh ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2511.09135

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.

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Type: preprint
Landing Page: http://arxiv.org/abs/2511.09135
PDF: https://arxiv.org/pdf/2511.09135
OA Status: green
OpenAlex ID: https://openalex.org/W4416236337

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DOI: https://doi.org/10.48550/arxiv.2511.09135

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

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Type: preprint

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Publication year: 2025

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Publication date: 2025-11-12

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Authors: Jieun Han, Daniel J. Lee, Haneul Yoo, Jun‐Hyoung Park, Soyeon Ahn, Alice Oh

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Landing page: https://arxiv.org/abs/2511.09135

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PDF URL: https://arxiv.org/pdf/2511.09135

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Open access: Yes

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OA status: green

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Cited by: 0

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