Enhancing Online English Education with Naive Bayes Algorithm-based Adaptive Learning System

In recent years, English online education has developed very fast, and smarter adaptive learning systems are needed. This article focuses on how the Naïve Bayes algorithm can improve digital English teaching by making it more automated, personalized, and efficient. Conventional platforms tend to use static content, providing students with the same exercises irrespective of their level of proficiency. Naïve Bayes, an established text classification model, allows for an adaptive learning environment by inspecting student inputs—written texts, quiz answers, or speech patterns—to identify mistakes, forecast difficulties, and deliver personalized exercises. For instance, the algorithm can rank grammatical mistakes (such as article or tense mistakes) in pupils' essays and give immediate corrections. It also identifies students' individual areas of strength and weakness in vocabulary or grammar, which impacts lesson planning accordingly. Aside from personalized comments, Naïve Bayes aids teachers in detecting typical problems between groups, making course development data-driven. Embodiment of Naïve Bayes in online English teaching systems unites conventional classroom approaches with the digital world, delivering more perceptive and engaging learning experiences. Future studies may integrate Naïve Bayes with other machine learning methods to enhance language learning models even further.