International Journal of Science, Engineering and TechnologyAuthors: Heena Yadav, Rakesh Verma, Seema
Abstract: The past decade witnessed remarkable growth in Artificial Intelligence (AI) research, and Natural Language Processing (NLP) emerged as a particularly prominent branch, focused on interpreting human communication in text and speech. NLP brought educational technology new possibilities, including the ability to grade student essays, provide feedback on mistakes, and guide the learning process through intelligent tutoring in real time. The present paper examined these possibilities from a theoretical standpoint and emphasised the importance of meaningful integration of NLP into contemporary educational systems. The study focused on developments in computational linguistics, deep learning, and transformer-based language models. It explored their applications in automated assessment, intelligent tutoring, sentiment analysis of learner feedback, and personalised instruction. A conceptual framework for the pipeline of linguistic processing, machine learning, and pedagogical feedback was introduced to explain how learner-generated text moves through the pipeline to foster adaptive learning. Despite the promising potential of these technologies, certain challenges were identified that require researchers' attention, namely the fairness of algorithmic assessment, the multilingual adaptability of systems, and the ethics of working with student data. Overall, the study's findings indicate the great promise of NLP as an integral part of future intelligent educational systems, provided its development adheres to rigorous technical and educational principles.
DOI: http://doi.org/10.5281/zenodo.21702569
