# Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines

arXiv: https://arxiv.org/abs/2402.01659 · HTML: https://mlsanity.com/paper/2402.01659/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 43 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [More than Model Documentation: Uncovering Teachers' Bespoke Information Needs for Informed Classroom Integration of ChatGPT](https://mlsanity.com/paper/2309.14458/) (2023)

## Built on by (most cited first, 12 of 12)

- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines](https://mlsanity.com/paper/2405.11800/) (2024)
- [Understanding Human-AI Trust in Education](https://mlsanity.com/paper/2506.09160/) (2025)
- [A Meta-Analysis of LLM Effects on Students across Qualification, Socialisation, and Subjectification](https://mlsanity.com/paper/2509.22725/) (2025)
- [Lessons for GenAI Literacy From a Field Study of Human-GenAI Augmentation in the Workplace](https://mlsanity.com/paper/2502.00567/) (2025)
- [Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education](https://mlsanity.com/paper/2412.02653/) (2024)
- [AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding](https://mlsanity.com/paper/2511.00265/) (2025)
- [Topic Discovery and Classification for Responsible Generative AI Adaptation in Higher Education](https://mlsanity.com/paper/2512.16036/) (2025)
- [Scalable Classification of Course Information Sheets Using Large Language Models: A Reusable Institutional Method for Academic Quality Assurance](https://mlsanity.com/paper/2603.13562/) (2026)
- [Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning](https://mlsanity.com/paper/2605.27400/) (2026)
- [Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction](https://mlsanity.com/paper/2606.18548/) (2026)
- [A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education](https://mlsanity.com/paper/2607.12296/) (2026)

## Abstract (arXiv)

The release of ChatGPT in November 2022 prompted a massive uptake of generative artificial intelligence (GenAI) across higher education institutions (HEIs). HEIs scrambled to respond to its use, especially by students, looking first to regulate it and then arguing for its productive integration within teaching and learning. In the year since the release, HEIs have increasingly provided policies and guidelines to direct GenAI. In this paper we examined documents produced by 116 US universities categorized as high research activity or R1 institutions to comprehensively understand GenAI related advice and guidance given to institutional stakeholders. Through an extensive analysis, we found the majority of universities (N=73, 63%) encourage the use of GenAI and many provide detailed guidance for its use in the classroom (N=48, 41%). More than half of all institutions provided sample syllabi (N=65, 56%) and half (N=58, 50%) provided sample GenAI curriculum and activities that would help instructors integrate and leverage GenAI in their classroom. Notably, most guidance for activities focused on writing, whereas code and STEM-related activities were mentioned half the time and vaguely even when they were (N=58, 50%). Finally, more than one half of institutions talked about the ethics of GenAI on a range of topics broadly, including Diversity, Equity and Inclusion (DEI) (N=60, 52%). Overall, based on our findings we caution that guidance for faculty can become burdensome as extensive revision of pedagogical approaches is often recommended in the policies.