Natural Language Enhancement for English Teaching Using Character-Level Recurrent Neural Network with Back Propagation Neural Network based Classification by Deep Learning Architectures

Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and have received increased attention from relevant education departments as integral aspect of new curriculum reform. The environment undergoing extraordinary changes a result constant improvement extension level scale, well growth Internet information technology. As result, current research aims to look into techniques efficiently using AI (artificial intelligence) apps teach learn perspective university students. This can measure levels effectiveness employment applications based on deep techniques. There, language enhancement has been carried out Character-level recurrent neural network with back Propagation (Cha_RNN_BPNN) classification. With help this DL (deep learning) technique, it possible use methods assist teachers in analysing diagnosing students' behaviour, replacing part answer questions timely manner, automatically grading assignments during process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level Readability, Cosine Similarity Semantic Coherence, gradient change NN, validation accuracy, training accuracy proposed technique.