Enhancing English Proficiency Test Evaluation: Leveraging Artificial Intelligence for Result Classification

With advancements in AI technology, there has been growing interest in employing its capabilities to generate improvements in different application domains such as medicine, industry, the transport sector, and education. In Education, AI has the potential to improve the efficiency and effectiveness of all educational processes. This study explores the application of AI algorithms to categorize test results and explore their potential benefits in the field of English proficiency assessment considering a four English proficiency test of a Mexican University. In our study, we evaluated a total of 15 different models for predicting english proficiency test results. After careful analysis and comparison of these models, we found that LightGBM (Light Gradient Boosting Machine) outperformed the others in terms of various performance metrics. Findings indicate that the integration of AI in English proficiency test result classification can significantly enhance the assessment process, benefiting candidates, institutions, and organizations involved in evaluating language proficiency. The research highlights the transformative potential of AI in revolutionizing how English language skills are evaluated, leading to fairer, more efficient, and more accurate assessments.