Automatic Detection of Inauthentic Templated Responses in English Language Assessments

Pearson Education, Inc. Author Note Correspondence concerning this article should be addressed to Lee Becker. Pearson affiliated authors can be reached at .@pearson.com. Sarah Hughes can be reached at sarah.hughes1@pearson.com. Joshua Southerland can be reached at josh.southerland@pearson.com In this study, we introduce the automated detection of inauthentic, templated responses (AuDITR) task, describe a machine learning-based approach to this task and illustrate the importance of regularly updating these models in production. Introduction English language proficiency (ELP) tests carry exceptionally high stakes because of how they influence access to employment, education and national residency status.