Artificial Intelligence (AI) is changing quickly, and this is having an effect on computational linguistics and its subfields, especially in the areas of Natural Language Understanding (NLU) and linguistic analysis. This study is a quantitative investigation designed to assess the role of AI in improving semantic interpretation, syntactic parsing, and contextual comprehension across various linguistic tasks. While previous studies have concentrated on theoretical frameworks and technological infrastructures, there exists a paucity of empirical research regarding the quantifiable impact of AI on NLU performance. The study utilizes a purposive sampling design; data will be gathered through structured questionnaires administered to 250 AI professionals, linguists, and computer science researchers. We used statistical tests, correlation, and regression analysis to see how AI-based methods affected the improvement of language accuracy, contextual adaptation, and language resource adaptation. The results indicated a significant positive correlation between AI integration and linguistic performance, with machine learning models demonstrating greater adaptability across multilingual datasets. The results also show that AI-based NLU systems make it easier to find mistakes, figure out what words mean, and use them in real time in education, healthcare, and translation services. The research serves as a gap-filling study by providing empirical evidence of the measurable benefits of AI in linguistic analysis. The study contributes to theoretical and practical discourse by delineating the potential of AI to mitigate the deficiencies of traditional language models and proposing recommendations for further application in multilingual and resource-constrained environments.
Tahir et al. (2025) studied this question.