Generating Reading Comprehension Exercises with Large Language Models for Educational Applications

• URL: http://arxiv.org/abs/2511.18860v1
• Date: Mon, 24 Nov 2025 08:00:48 GMT
• Title: Generating Reading Comprehension Exercises with Large Language Models for Educational Applications
• Authors: Xingyu Huang, Fei Jiang, Jianli Xiao,
• Abstract summary: This paper proposes a new framework, which is named as Reading Exercise Generation (RCEG)<n>RCEG can generate high-quality and personalized English reading comprehension exercises automatically.<n>To evaluate the performance of RCEG, a dedicated dataset for English reading comprehension is constructed.
• Score: 6.823539126000323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of large language models (LLMs), the applications of LLMs have grown substantially. In the education domain, LLMs demonstrate significant potential, particularly in automatic text generation, which enables the creation of intelligent and adaptive learning content. This paper proposes a new LLMs framework, which is named as Reading Comprehension Exercise Generation (RCEG). It can generate high-quality and personalized English reading comprehension exercises automatically. Firstly, RCEG uses fine-tuned LLMs to generate content candidates. Then, it uses a discriminator to select the best candidate. Finally, the quality of the generated content has been improved greatly. To evaluate the performance of RCEG, a dedicated dataset for English reading comprehension is constructed to perform the experiments, and comprehensive evaluation metrics are used to analyze the experimental results. These metrics include content diversity, factual accuracy, linguistic toxicity, and pedagogical alignment. Experimental results show that RCEG significantly improves the relevance and cognitive appropriateness of the generated exercises.

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