Exploring the effectiveness of natural language processing technologies in automatically generating English teaching resource content

Abstract

In the digital era, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) has progressively enabled the automation of English teaching resource generation. Traditional methods for producing teaching materials typically require extensive manual effort, which limits their scalability and flexibility to accommodate standard learning needs. However, NLP technologies present an effective solution by allowing for the automatic generation of high-quality instructional content. This research highlights the capacity of NLP to generate organized and relevant materials for English language teaching. An Efficient Glowworm Swarm Optimized Bidirectional Encoder Representations from Transformers (EGWS-BERT) method is employed to measure the accuracy and flexibility of the produced content. Publicly available lesson plans and grammar guides serve as data sources, which undergo pre-processing methods such as data cleaning and text tokenization. The NLP framework emphasizes extracting key information through automated summarization, refining language meaning by recognizing and improving keywords, and ensuring coherence and usability through organized content integration into e-learning platforms. Results indicate that the proposed model significantly enhances content generation effectiveness, reducing the manual workload while producing high-quality educational resources. Compared to current methods, the suggested EGWS-BERT technique yields high-quality instructional content with 96% accuracy and 94% recall, which is an outstanding outcome. The findings suggest that NLP-driven approaches can assess resource development, providing scalable, adaptable, and effective solutions for English language teaching.