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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.08355 |
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| _version_ | 1866914031277703168 |
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| author | Samant, Yashad Becker, Lee Hellman, Scott Behan, Bradley Hughes, Sarah Southerland, Joshua |
| author_facet | Samant, Yashad Becker, Lee Hellman, Scott Behan, Bradley Hughes, Sarah Southerland, Joshua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Detection of Inauthentic Templated Responses in English Language Assessments Samant, Yashad Becker, Lee Hellman, Scott Behan, Bradley Hughes, Sarah Southerland, Joshua Computation and Language Artificial Intelligence 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. |
| title | Automatic Detection of Inauthentic Templated Responses in English Language Assessments |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.08355 |