  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.
