Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids

• URL: http://arxiv.org/abs/2511.02875v1
• Date: Tue, 04 Nov 2025 06:24:47 GMT
• Title: Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids
• Authors: R. Yamamoto Ravenor,
• Abstract summary: This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As generative AI diffuses through academia, policy-practice divergence becomes consequential, creating demand for auditable indicators of alignment. This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use. The framework extracts empirical and epistemic signals from academics, yielding three filtered indicators of such voids: (1) AI-integrated assessment capacity (proxy) - within a three-signal screen (AI skill, perceived teaching benefit, detection confidence), the share who would fully allow AI in exams; (2) sector-level necessity (proxy) - among high output control users who still credit AI with high contribution, the proportion who judge AI capable of challenging established disciplines; and (3) ontological stance - among respondents who judge AI different in kind from prior tools, report practice change, and pass a metacognition gate, the split between material and immaterial views as an ontological map aligning procurement claims with evidence classes.

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