{"ID":2846088,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02875","arxiv_id":"2511.02875","title":"Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids","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.","short_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 practiti...","url_abs":"https://arxiv.org/abs/2511.02875","url_pdf":"https://arxiv.org/pdf/2511.02875v1","authors":"[\"R. Yamamoto Ravenor\"]","published":"2025-11-04T06:24:47Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\"]","methods":"[]","has_code":false}