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

TL;DR

This paper introduces a novel method for creating personalized reading comprehension tests for EFL learners by using GPT-4 to generate interest-aligned passages, improving engagement and comprehension.

Contribution

It presents a structured content transcreation pipeline leveraging GPT-4 to generate personalized reading materials tailored to individual interests in EFL education.

Findings

Personalized passages improve comprehension.

Interest-aligned materials boost motivation.

Enhanced engagement leads to better learning outcomes.

Abstract

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.