Designing an NLP-Powered Adaptive Learning Environment for Diverse English Language Learners

The study introduces an innovative NLP-powered adaptive learning environment tailored for diverse English language learners. Utilizing natural language processing (NLP) technologies including text and speech analysis the proposed system provides a tailored curriculum comprised of content tailored to a given language strengths and cultural differences. Using tokenization and TF-IDF feature creator, the study preprocesses text data for many NLP works. The research leads to the creation of an adaptive learning environment, incorporating natural language processing technologies to personalize content, provide immediate feedback, and administer skill-based assessments in immersive spaces. The study measures the performance of the system using accuracy, precision, recall and F1-score. The study shows outstanding accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0. 6 \%}$</tex>, it shows the system is efficient in creating an accessible and engaging foreign language learning experience. The novelty of this study arises from the fusion of TF-IDF representation with a deep neural network, proposing a strong and context-dependent model for language proficiency classification. Future research involves improving adaptive learning model based on ongoing feedback, investigating further language features and checking on rising technologies to improve the learning experience even more immersive language learning. This study represents a breakthrough in developments for adaptive and inclusive language education, with potential practical applications for educators, researchers and developers working to change language learning methodologies.