2023/09/22 by Xizhe Wang, Yihua Zhong, Wang, Xizhe +5 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Online Learning and Analytics #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2309.12808
openalex publication_date 2023/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As a common approach to learning English, reading comprehension primarily entails reading articles and answering related questions. However, the complexity of designing effective exercises results in students encountering standardized questions, making it challenging to align with individualized learners' reading comprehension ability. By leveraging the advanced capabilities offered by large language models, exemplified by ChatGPT, this paper presents a novel personalized support system for reading comprehension, referred to as ChatPRCS, based on the Zone of Proximal Development theory. ChatPRCS employs methods including reading comprehension proficiency prediction, question generation, and automatic evaluation, among others, to enhance reading comprehension instruction. First, we develop a new algorithm that can predict learners' reading comprehension abilities using their historical data as the foundation for generating questions at an appropriate level of difficulty. Second, a series of new 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 ability and reading comprehension prompt patterns, ChatPRCS is systematically validated through 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.