← 研踪 PaperTrailarXiv:2611.01330 · v1
From Policing Provenance to Designing for Evidence: Testing Four Propositions on Generative AI Against Research and Institutional Policy
首次提交 2026-11-01 06:54
版本更新 2026-11-01 06:54
雷达观测 2026-11-04 23:43
摘要
Generative AI has made it hard to establish whether a student produced a piece of assessed work. Building on the argument that validity matters more than cheating, this paper tests four propositions drawn from a strongly stated position: that redesign is more sustainable than detecting or banning AI; that asking students whether and how much AI they used yields low-value information; that unaided ability should be tested directly in controlled conditions; and that the same logic applies to peer review. The propositions are tested against two purposively sampled, coded corpora: 41 research and sector sources and 55 university and sector policy documents. Coding was by a single coder and the policy sample is UK-weighted, so counts describe the sample, not the sector. The propositions hold unevenly. Redesign over detection is well supported, but only as redesign plus assurance: 4 of 55 documents prohibit AI by default, though about 10 of the 55 are publisher or editorial policies that govern authors rather than student assessment; of the 23 that take a position on detection, 16 reject detectors and one requires them. Testing unaided ability is supported as one lane within a programme, not as a return to invigilation. The disclosure proposition is complicated: institutions treat disclosure as valuable, yet the limited evidence suggests many students do not complete required declarations. The peer-review extension is plausible but under-examined. The field is converging on graded permission scales and two-lane designs, and 31 of 55 documents define named categories of use. From these findings the paper builds a nine-step decision chain that extends outcome-first design logic: the role of AI is decided after the evidence an outcome requires. It also separates design moves that add temporary friction from those that provide assurance. A worked example and the full policy matrix are included.
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v1 · 2026-11-01 06:54 · 最新观测版本
From Policing Provenance to Designing for Evidence: Testing Four Propositions on Generative AI Against Research and Institutional Policy
Generative AI has made it hard to establish whether a student produced a piece of assessed work. Building on the argument that validity matters more than cheating, this paper tests four propositions drawn from a strongly stated position: that redesign is more sustainable than detecting or banning AI; that asking students whether and how much AI they used yields low-value information; that unaided ability should be tested directly in controlled conditions; and that the same logic applies to peer review. The propositions are tested against two purposively sampled, coded corpora: 41 research and sector sources and 55 university and sector policy documents. Coding was by a single coder and the policy sample is UK-weighted, so counts describe the sample, not the sector. The propositions hold unevenly. Redesign over detection is well supported, but only as redesign plus assurance: 4 of 55 documents prohibit AI by default, though about 10 of the 55 are publisher or editorial policies that govern authors rather than student assessment; of the 23 that take a position on detection, 16 reject detectors and one requires them. Testing unaided ability is supported as one lane within a programme, not as a return to invigilation. The disclosure proposition is complicated: institutions treat disclosure as valuable, yet the limited evidence suggests many students do not complete required declarations. The peer-review extension is plausible but under-examined. The field is converging on graded permission scales and two-lane designs, and 31 of 55 documents define named categories of use. From these findings the paper builds a nine-step decision chain that extends outcome-first design logic: the role of AI is decided after the evidence an outcome requires. It also separates design moves that add temporary friction from those that provide assurance. A worked example and the full policy matrix are included.
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