This research presents a new AI-driven approach to optimizing English language learning with a TF-IDF-based Gated Recurrent Unit model. It provides an effective framework for assessing and improving student writing competency by using advanced text classification approaches. Here, the six criteria evaluated on the basis of the ELLIPSE corpus of essays by grade 8-12 learners include coherence, syntax, vocabulary, phraseology, grammar, and conventions. Extensive preprocessing in terms of tokenization, stop word removal, stemming, and lemmatization, TF-IDF extracts key text features into numerical representations and feed into a GRU model that captures long-term dependencies as well as contextual meaning followed by classification with a dense layer and softmax activation. It has been implemented in Python: The GRU model yields an accuracy of 99.7%, precision at 99.04%, recall at 99.56%, and F1-score of 99.54. The work provides a rigorous methodology for text classification which improves the students’ writing skills and propels AI tools in education.