Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913967296741376 |
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| author | Xie, Yangxinyu Chen, Xuyang Ren, Zhimei Su, Weijie J. |
| author_facet | Xie, Yangxinyu Chen, Xuyang Ren, Zhimei Su, Weijie J. |
| contents | As artificial intelligence tools become ubiquitous in education, maintaining academic integrity while accommodating pedagogically beneficial AI assistance presents unprecedented challenges. Current AI detection systems fail to control false positive rates (FPR) and suffer from bias against minority student groups, prompting institutional suspensions of these technologies. Watermarking techniques offer statistical rigor through precise $p$-values but remain untested in educational contexts where students may use varying levels of permitted AI edits. We present the first adaptation of watermarking-based detection methods for classroom settings, introducing conformal methods that effectively control FPR across diverse classroom settings. Using essays from native and non-native English speakers, we simulate seven levels of AI editing interventions--from grammar correction to content expansion--across multiple language models and watermarking schemes, and evaluate our proposal under these different setups. Our findings provide educators with quantitative frameworks to enforce academic integrity standards while embracing AI integration in the classroom. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection Xie, Yangxinyu Chen, Xuyang Ren, Zhimei Su, Weijie J. Applications As artificial intelligence tools become ubiquitous in education, maintaining academic integrity while accommodating pedagogically beneficial AI assistance presents unprecedented challenges. Current AI detection systems fail to control false positive rates (FPR) and suffer from bias against minority student groups, prompting institutional suspensions of these technologies. Watermarking techniques offer statistical rigor through precise $p$-values but remain untested in educational contexts where students may use varying levels of permitted AI edits. We present the first adaptation of watermarking-based detection methods for classroom settings, introducing conformal methods that effectively control FPR across diverse classroom settings. Using essays from native and non-native English speakers, we simulate seven levels of AI editing interventions--from grammar correction to content expansion--across multiple language models and watermarking schemes, and evaluate our proposal under these different setups. Our findings provide educators with quantitative frameworks to enforce academic integrity standards while embracing AI integration in the classroom. |
| title | Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection |
| topic | Applications |
| url | https://arxiv.org/abs/2507.23113 |