AI Transformations in Language Acquisition and Linguistic Study

Abstract: Language, a cornerstone of human communication and cognition, has long been a central focus of linguistic research. Advances in Artificial Intelligence (AI), including Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL), have transformed the methodologies and tools available to linguists, enabling more profound insights into language acquisition, structure, and usage. This study investigates integrating AI-driven techniques into linguistics, particularly in understanding and modelling language acquisition processes. It explores how AI enhances the study of syntax, semantics, and pragmatics while addressing the benefits and limitations of these tools in linguistic research. The paper critically reviews existing literature, comparing traditional linguistic theories with AI methodologies such as SRNs, connectionist models, and transformer-based approaches like BERT and GPT. It examines the applications of these tools in analyzing linguistic data, modelling language learning, and automating assessment processes. AI models significantly improve the simulation and understanding of language learning mechanisms, offering insights into cognitive processes, developmental trajectories, and common learner errors. Tools driven by NLP and ML automate complex tasks, such as parsing and sentiment analysis, enhancing efficiency and precision in linguistic research. However, challenges such as data bias, model interpretability, and ethical considerations persist. AI has revolutionized linguistics by providing robust methodologies for analyzing language at unprecedented scales and depths. While offering transformative tools for research, its limitations underscore the need for interdisciplinary collaboration and ethical guidelines to ensure responsible use. Integrating AI in linguistics facilitates new research directions, enabling scholars to uncover hidden patterns in language data and refine theoretical models. Future studies should prioritize diverse data collection, ethical considerations, and the development of interpretable AI systems to harness the full potential of these technologies in advancing linguistic understanding.