An Analysis of Artificial Intelligence Applications in Educational Linguistics for Information Retrieval and Academic Text Analytics

Abstract

The development of Artificial Intelligence (AI)  technologies has greatly influenced the field of educational linguistics as it offers advanced mechanisms for linguistic data processing, information retrieval, text analysis in academia, and individual learning assistance. This paper aims to offer an in-depth analysis of the use of AI in educational linguistics, paying attention to the implementation of Natural Language Processing (NLP), machine learning, and language models for academic information processing and linguistic assessment. A conceptual pipeline based on AI is designed to show all the stages involved in linguistic data processing, from obtaining a textual corpus, pre-processing, feature and embeddings extraction, through model processing to evaluation. Major areas of AI application in the field under discussion include automatic essay scoring, semantic information retrieval, corpus-based linguistic analysis, sentiment and feedback analysis, and generative AI-assisted academic writing. It was found out that AI-based systems enhance efficiency, consistency, and scalability of linguistic assessment and academic document
retrieval due to the implementation of contextual embeddings, deep learning and transformer models. Yet, the obstacles that
pertain to issues of interpretability, linguistic biases, transparency, diversity of data, and alignment with pedagogy cannot be overlooked and serve as considerable hurdles for practical use. The study draws attention to the need for creating accountable, transparent, and multilingual AI tools that would help educators, not substitute their human competence. Research avenues focus on cross-linguistic validation, standardization of evaluation processes, and collaboration between AI researchers and language education experts.