A hybrid fuzzy neural model for corpus-based English language teaching

Abstract English language teaching that is corpus-based uses authentic language data as a basis for teaching students vocabulary, grammar, or sentence structure. However, traditional systems are unable to account for ambiguous meanings, uncertain grammatical rules, and differences in knowledge from student to student. This research presents a new framework, HFLEM (Hybrid Fuzzy Logic for English Modeling), that represents an innovation in English language teaching through a hybrid model that brings together fuzzy logic and neural networks. HFLEM operates in the domain of natural language processing and artificial intelligence. Additionally, HFLEM focuses on ambiguous language patterns and the individualized learning experience. The HFLEM model utilizes fuzzy logic, or logic that can interpret ambiguous or vague language, to come to interpret language. Neural networks that can learn grammatical and semantic patterns from the large amount of language that is kept in English corpora are also a component of the HFLEM model. This hybrid approach, where fuzzy logic models the nuanced, imprecise aspects of language while the neural network handles complex, data-driven patterns, enables the system to evaluate student responses and provide more accurate, personalized, and intelligent feedback. HFLEM was tested on a dataset of student writing samples, achieving an 18.3% increase in semantic error detection accuracy, allowing the system to pinpoint better subtle mistakes that evade conventional tools and a 16.5% improvement in grammar correction efficiency compared to conventional corpus-based models. HFLEM can be applied in smart classrooms, digital learning platforms, AI-driven tutoring systems, and automated language assessment tools.