CORPUS-BASED LINGUISTIC ANALYSIS'S EFFECT ON PROFESSIONAL LANGUAGE PROFICIENCY
Abstract
This study looks at how well 80 undergraduate artificial intelligence students at Fergana State Technical University can improve their professional language proficiency through corpus-based linguistic analysis. Students in the experimental group used specialized corpus linguistics tools, such as AntConc and Sketch Engine, to conduct practical analysis of real AI-related texts over the course of a semester. Through frequency lists, lemmatization, collocation identification, and topic modeling, these tools allowed students to investigate language data, offering profound insights into the use of phraseology and vocabulary specific to a given domain. The control group, on the other hand, adhered to the conventional curriculum, which emphasized instructor-led activities and theoretical instruction without exposure to corpus technologies. Assessments conducted before and after the intervention evaluated students' ability to use technical terms, form professional sentences, and identify collocations that are pertinent to the field of artificial intelligence.