Australian universities regulate students' use of generative artificial intelligence (GenAI) through overlapping policies, procedures, guidance, and assessment instructions, but these environments may classify identical conduct differently. We applied 15 standardized student-use vignettes to the public policy environments of 20 Australian universities and produced 300 university-case classifications. We analyzed binding instruments (Layer A) separately from the full official environment (Layer B), then classified each combination as clearly permitted, permitted with conditions, potential policy breach, clearly prohibited, or indeterminate. We measured cross-university divergence with normalized Shannon entropy. We classified 120 combinations (40.0%) as clearly prohibited, 97 (32.3%) as potential policy breaches, 27 (9.0%) as permitted with conditions, and 56 (18.7%) as indeterminate; none met the strict threshold for clearly permitted. Disclosed language rewriting and a disclosed AI-drafted paragraph produced the highest divergence, whereas an explicit assessment prohibition produced unanimity. Binding instruments remained silent on GenAI in 100 combinations, and guidance clarified 88. The primary researcher led the coding with AI assistance and manually reviewed all 201 queued rows. An independent human second coder, who used no AI assistance, coded a blind 75-row sample. Overall agreement reached 57.3% (unweighted Cohen's kappa = 0.395; bootstrap 95% CI [0.244, 0.539]). The findings distinguish permission-gated from disclosure-based architectures and show that written policies regulate the retention of AI-generated text more clearly than process-only assistance does. Comparative policy-vignette testing evaluates whether written governance supports defensible classifications; it does not predict misconduct or enforcement decisions.

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