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arXiv · 2608.11256

Why AI Detection Fails for Academic Integrity

Jonathan A. Karr Jr·Grigorii Khvatskii·Ting Hua·Nitesh V. Chawla
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Abstract

Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled study of published English abstracts (four domains; 2013 to 2015 vs. 2023 to 2025), we quantify this policy failure under proxy human/AI labels at tau=0.50. Light "refine abstract only" edits, a proxy for guideline-compliant AI assistance, are flagged at 38 to 80%. Unmodified 2023 to 2025 originals are flagged at 9 to 15%, with non-STEM rates far above STEM (p<0.001); elevated scores track long-token and Academic Word List density, not authorship intent alone. After Undetectable AI humanization, evasion is near-total: fewer than 4% of AI-labeled rewrites remain flagged (post-humanization detection rate <4%; FNR >96%). Honest AI-editing results in a higher sanction risk than humanizer-assisted evasion. Therefore, detector scores should not serve as standalone misconduct evidence.

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Jonathan A. Karr Jr, Grigorii Khvatskii, Ting Hua, Nitesh V. Chawla. 2026-08-20. Why AI Detection Fails for Academic Integrity. https://arxiv.org/abs/2608.11256

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