# Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines

arXiv: https://arxiv.org/abs/2405.11800 · HTML: https://mlsanity.com/paper/2405.11800/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 5 of 5)

- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines](https://mlsanity.com/paper/2402.01659/) (2024)
- [Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines](https://mlsanity.com/paper/2312.05235/) (2023)
- [Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education](https://mlsanity.com/paper/2305.18617/) (2023)
- [Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle](https://mlsanity.com/paper/2312.00087/) (2023)

## Built on by (most cited first, 13 of 13)

- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality](https://mlsanity.com/paper/2412.07200/) (2024)
- [Understanding Human-AI Trust in Education](https://mlsanity.com/paper/2506.09160/) (2025)
- [Designing AI-Resilient Assessments Using Interconnected Problems: A Theoretically Grounded and Empirically Validated Framework](https://mlsanity.com/paper/2512.10758/) (2025)
- [What Students Ask, How a Generative AI Assistant Responds: Exploring Higher Education Students' Dialogues on Learning Analytics Feedback](https://mlsanity.com/paper/2601.04919/) (2026)
- ["We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools](https://mlsanity.com/paper/2410.21358/) (2024)
- [Defining a Strategic Action Plan for AI in Higher Education](https://mlsanity.com/paper/2510.03343/) (2025)
- [Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids](https://mlsanity.com/paper/2511.02875/) (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)
- [Multi-Level Barriers to Generative AI Adoption Across Disciplines and Professional Roles in Higher Education](https://mlsanity.com/paper/2603.27052/) (2026)
- [Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study](https://mlsanity.com/paper/2603.27777/) (2026)
- [AI in Work-Based Learning: Understanding the Purposes and Effects of Intelligent Tools Among Student Interns](https://mlsanity.com/paper/2603.28786/) (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)

Integrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. Yet a thorough understanding of the global institutional adoption policy remains absent, with most of the prior studies focused on the Global North and the promises and challenges of GAI, lacking a theoretical lens. This study utilizes the Diffusion of Innovations Theory to examine GAI adoption strategies in higher education across 40 universities from six global regions. It explores the characteristics of GAI innovation, including compatibility, trialability, and observability, and analyses the communication channels and roles and responsibilities outlined in university policies and guidelines. The findings reveal a proactive approach by universities towards GAI integration, emphasizing academic integrity, teaching and learning enhancement, and equity. Despite a cautious yet optimistic stance, a comprehensive policy framework is needed to evaluate the impacts of GAI integration and establish effective communication strategies that foster broader stakeholder engagement. The study highlights the importance of clear roles and responsibilities among faculty, students, and administrators for successful GAI integration, supporting a collaborative model for navigating the complexities of GAI in education. This study contributes insights for policymakers in crafting detailed strategies for its integration.