Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids
It addresses the issue of misalignment between AI policies and actual use in academic settings, which is incremental as it builds on existing frameworks for auditing and indicator development.
This study tackled the problem of policy-practice divergence in academia regarding generative AI use by developing a ten-item instrument to identify voids between institutional rules and practitioner practices, resulting in three filtered indicators such as the share of academics who would fully allow AI in exams.
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.