Language Proficiency

Language proficiency assessment research aims to accurately and efficiently measure individuals' language skills, focusing on both spoken and written modalities. Current efforts leverage machine learning, particularly large language models (LLMs) and semi-supervised learning techniques, to automate scoring and analyze linguistic features like pronunciation, fluency, and vocabulary, often incorporating techniques like Dynamic Time Warping and information-theoretic approaches. These advancements offer significant potential for improving the efficiency and objectivity of language proficiency tests, impacting fields like education, immigration, and international communication.