Proficiency Vector

Proficiency vectors represent a learner's skill level across various aspects of language, aiming to automate the assessment of language proficiency in both written and spoken forms. Current research heavily utilizes large language models (LLMs) and machine learning techniques, often incorporating features like grammatical accuracy, vocabulary, fluency, and pronunciation to generate these vectors, with some work exploring hierarchical relationships between concepts. This automated assessment offers significant potential for improving the scalability and efficiency of language testing, providing valuable insights for educators and clinicians alike, while also raising important questions about the reliability and validity of AI-driven evaluations.