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

arXiv: https://arxiv.org/abs/2511.02875 · HTML: https://mlsanity.com/paper/2511.02875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines](https://mlsanity.com/paper/2405.11800/) (2024)
- [AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories](https://mlsanity.com/paper/2308.15119/) (2023)

## Abstract (arXiv)

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.