Generating Reading Comprehension Exercises with Large Language Models for Educational Applications
This work addresses the need for personalized and high-quality educational content generation, but it is incremental as it builds on existing LLM capabilities for a specific domain.
The paper tackles the problem of automatically generating English reading comprehension exercises for educational applications by proposing a new LLM framework called RCEG, which uses fine-tuned models and a discriminator to improve quality, resulting in significant improvements in relevance and cognitive appropriateness as measured by metrics like content diversity and factual accuracy.
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.