Automatic Detection of Inauthentic Templated Responses in English Language Assessments
Yashad Samant, Lee Becker, Scott Hellman, Bradley Behan, Sarah Hughes, Joshua Southerland
Published: 2025/9/10
Abstract
In high-stakes English Language Assessments, low-skill test takers may employ memorized materials called ``templates'' on essay questions to ``game'' or fool the automated scoring system. 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.