Leveraging AI-Based Large Language Models to Strengthen English Vocabulary Skills in EFL Students

The concepts of AI and LLMs brought drastic changes to the sphere of educational technology, especially about the improvement of language teaching efficiency. Using elaborate computational algorithms and massive data, these models provide unprecedented potential for the identification and creation of related contexts hence are very efficient instruments in building up English vocabulary in students learning English as a Foreign Language (EFL). In this paper, development of AI base vocabulary learning system with findings of advancement over the traditional and other modern methods in Python has been discussed. The indicated system relies on the GPT-4 model that was proved to offer natural language processing at a level higher than its predecessors thus differentiating the proposed vocabulary exercises based on context. The system also employs some of the modern practices like word embeddings (Word2Vec, GloVe), contextualized embeddings (BERT) and reinforcement learning to make it a dynamic learning system. The feasibility of the above-proposed AI-based system is assessed from the qualitative as well as quantitative analysis. Quantitative feedback is supported by qualitative feedback where results indicate higher student satisfaction while using the system. In general, the paper stands upon the idea that the proposed AI-based system could really improve the results of the study of English vocabulary with the benefits of this system towards traditional ones. This approach is indeed what can be described as the real progress in the field of educational technology, which provides a promising ground for the development of a strengthened approach to vocabulary instruction in EFL educational contexts.