Ethical Implications and Emerging Opportunities of Machine Learning Models in Automated English Language Testing

Abstract

Automated English language testing is becoming a prevalent trend as the educational institutions in need of scalable, objective, and effective assessment systems. Nevertheless, the models currently employed tend to lack reliability, transparency, as well as fairness, which restricts their use on high-stakes testing. The purpose of this work is to create a powerful machine learning based on the English proficiency assessment system, which combines linguistic characteristics with contextual embeddings to increase the level of accuracy and decrease bias. The novelty of the work is a combination of the BERT-based deep language representations with the ethical protection against the unfairness of fair automated scoring. The suggested framework was found to be 98% accurate, which is much better than other old methods like e-rater and AES models. Comparative analysis showed that there was an improvement in error management, similarity in varied inputs and decreased variation in prediction results. These findings underscore the capability of a developed NLP models in standard test. Finally, the work can add to the literature a scalable and ethically matched framework that will provide accuracy, fairness, and practicality in English language assessment.

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