Reading comprehension is a widely adopted method for learning English, involving reading articles and answering related questions. However, the reading comprehension training typically focuses on the skill level required for a standardized learning stage, without considering the impact of individual differences in linguistic competence. This paper presents a personalized support system for reading comprehension, named ChatPRCS, based on the Zone of Proximal Development (ZPD) theory. It leverages the advanced capabilities of large language models (LLMs), exemplified by ChatGPT (Chat Generative Pre-trained Transformer). ChatPRCS employs methods including skill prediction, question generation, and automatic evaluation, to enhance reading comprehension instruction. Firstly, a ZPD-based algorithm is developed to predict students' reading comprehension skills. This algorithm analyzes historical data to generate questions with appropriate difficulty. Second, a series of ChatGPT prompt patterns is proposed to address two key aspects of reading comprehension objectives: question generation, and automated evaluation. These patterns further improve the quality of generated questions. Finally, by integrating personalized skill prediction and reading comprehension prompt patterns, ChatPRCS is validated through a series of experiments. Empirical results demonstrate that it provides learners with high-quality reading comprehension questions that are broadly aligned with expert-crafted questions at a statistical level. Furthermore, this study investigates the effect of the system on learning achievement, learning motivation and cognitive load, providing further evidence of its effectiveness in instructing English reading comprehension.

Original languageEnglish
Pages (from-to)1762-1776
Number of pages15
JournalIEEE Transactions on Learning Technologies
Publication statusPublished - 27 May 2024


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