Background In reality, the English language is a mystery; despite its inherent worth and the advantages of fluency, there is a pervasive impression that English instruction in secondary schools is of low quality, contributing to students’ lack of proficiency in the language in higher education and beyond. Pedagogical approaches persist in the classroom, topic after subject, including English. Analyzing texts in great depth via translation and emphasizing vocabulary are joint exercises in English classes. Students waste a lot of time copying things off the board in English classes despite the growing recognition of the significance of both listening and speaking effectively. Methods The Fuzzy Bayesian Intelligent Tutoring System (FB-ITS) is an artificial intelligence (AI) system that adaptively supports students in English teaching and learning settings. It is built in this experimental research employing AI methodologies based on fuzzy logic and the Bayesian network methodology. Using conventional approaches that rely primarily on numerical scores to evaluate academics’ teaching and research activities at various levels is becoming increasingly challenging. Expert systems based on fuzzy logic, suggested in this study, can handle teacher and student evaluations even when faced with imprecise information and uncertainty; this is necessary since academic performance is being indexed in multiple international databases using impact indices at different scales. Results The results showed that, on average, students using the FB-ITS took less time to complete the post-test than students using the conventional e-learning system. This research proposes an English teaching and learning approach that has been very successful based on experimental findings of related big data clustering algorithms. The assessment accuracy has risen by 4%, and the teaching resource utilization rate has been increased by 5%.
Wen et al. (Thu,) studied this question.