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

TL;DR

This paper tackles policy-practice voids in academia arising from the diffuse adoption of generative AI by proposing a compact ten-item indirect-elicitation instrument embedded in a structured interpretive framework. It defines three analyses—AI-integrated assessment capacity, sector-level necessity, and ontological stance—to surface empirical and epistemic signals of misalignment between policy and practice. Pilot results illustrate how these indicators reveal practitioner capacities, perceived necessity, and ontological commitments, informing assessment pilots, curriculum redesign, and procurement coherence. The approach offers a reusable, auditable scaffold for institutions to monitor, compare cohorts, and align procurement with coherent evidentiary standards in AI-enabled higher education.

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.