Exploring the Application of Large Language Models in English Reading and Writing Courses: Technical Challenges and Development Opportunities

As large language models (LLMs) evolve rapidly in natural language processing, their application potential in educational scenes has become increasingly prominent. This article aims to explore the specific application and technical realization of LLMs in English reading and writing courses and solve the issues in practical teaching. Focusing on the core tasks of automatic composition correction, reading material generation and conversational writing guidance, this paper proposes an improved framework utilizing conditional Generative Adversarial Networks (cGAN). This framework improves the quality and controllability of model output by introducing conditional embedding layers, strengthening learning reward mechanisms and adaptive difficulty generation algorithms. The results show that, compared with the benchmark models such as BERT and T5, the proposed model improves accuracy by 25.51% at the highest, and the generation efficiency is also significantly optimized under large-scale data sets. In addition, a questionnaire survey of 50 teachers and 100 students shows that more than 85% of users are satisfied with the usability and practicability of the system.