One-Topic-Doesn't-Fit-All: Transcreating Reading Comprehension Test for Personalized Learning
This addresses engagement issues for EFL learners, but it is incremental as it applies existing AI methods to personalize educational content.
The authors tackled the problem of low engagement in English reading comprehension by generating personalized reading passages and questions aligned with students' interests using a transcreation pipeline with GPT-4o, resulting in improved 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 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.