Field Observations on AI-Detection Practices in Academic Institutions and a Proposal for Process-Based Academic Integrity Verification

An open letter to UNESCO's Section for Higher Education proposing the replacement of automated AI-detection regimes in academic institutions with a process-based verification framework. The letter argues that current AI-detection tools (Turnitin AI, GPTZero, Copyleaks, Sapling, Winston AI, and others) suffer from two compounding failures: (1) technical non-falsifiability — no detector establishes a physically verifiable signature of machine origin, and the author demonstrates from personal practice that heavily AI-assisted work can reliably produce near-zero detection scores; and (2) inverse incentive structure — the regime systematically punishes pedagogically encouraged AI use while a paid grey-market economy enables wholesale fraud to pass undetected. A controlled empirical battery (companion dataset, DOI 10.5281/zenodo.20094765) supplies the load-bearing evidence: 26 canonical human texts written between 81 BCE and 1962 CE, run through five contemporary detectors. One commercial tool classifies 92% of these pre-LLM canonical texts as AI-generated; another classifies 100% of modern human academic writing as AI but 0% of the same canonical corpus. These two failure patterns cannot be reconciled by any threshold adjustment and falsify the institutional claim that detection scores constitute physical evidence of authorship. The proposed alternative anchors academic integrity on the person rather than on the text, via three operational components: (a)post-submission oral verification (a scaled-down thesis-defense model); (b) argument-level overlap detection (semantic-embedding and argument-graph methods comparing submitted reasoning structures against existing literature, rather than surface text); and (c) institutional adoption of the publication-system standard already used by Nature, Science, IEEE, ACM, and the major Elsevier and Springer titles. The letter also addresses a structurally identical failure at the other end of the academic pipeline: predatory first-authorship practices in research-intensive doctoral programs. The same students whose intellectual labour is misread as AI-generated at the entry end are systematically dispossessed of first-authorship at the exit end. Documented cases (Tao Chongyuan, 2018; Lu Jingwei, 2018; Xiangya 2024; Huazhong Agricultural University 11-student joint denunciation, 2024; Chang'an University 1,000-page filing, 2021) establish the pattern. The letter concludes with a request for UNESCO to convene an international working group on assessment-method transition, building on the principles laid out in the 2023 Guidance for generative AI in education and research. Bilingual archive: English (primary) plus Chinese companion translation. Technical Report TR-AIDET-2026-01 and Evidence Annex are bundled.