# A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education

arXiv: https://arxiv.org/abs/2607.12296 · HTML: https://mlsanity.com/paper/2607.12296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines](https://mlsanity.com/paper/2405.11800/) (2024)
- [Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines](https://mlsanity.com/paper/2402.01659/) (2024)
- [Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education](https://mlsanity.com/paper/2305.18617/) (2023)
- [Analysis of Generative AI Policies in Computing Course Syllabi](https://mlsanity.com/paper/2410.22281/) (2024)
- [Engineering Educators' Perspectives on the Impact of Generative AI in Higher Education](https://mlsanity.com/paper/2502.00569/) (2025)

## Abstract (arXiv)

With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning are significant. At the same time, instructors have also been forced to address the use of GenAI as students have started to use it for a range of functions. Currently, comparative analysis of guidance provided by institutions and its uptake in instruction is lacking. In this paper we bridge this gap by comparing institutional and computing course level guidance to better understand this terrain. We utilize secondary analysis of institutional and course syllabi guidelines from higher education institutions in the U.S. classified as research-intensive. Our findings reveal that although institutional guidance is more pro-use, at the course-level the uptake is still guarded. We discuss the implications and propose an instructor-centered framework to guide future adoption of GenAI.