# The global landscape of academic guidelines for generative AI and Large Language Models

arXiv: https://arxiv.org/abs/2406.18842 · HTML: https://mlsanity.com/paper/2406.18842/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 63 references that are in the map.
- First posted on arXiv 26 May 2024.

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

- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [An Overview on Language Models: Recent Developments and Outlook](https://mlsanity.com/paper/2303.05759/) (2023)

## Built on by (most cited first, 4 of 4)

- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)
- [A Scoping Review of Large Language Model-Based Pedagogical Agents](https://mlsanity.com/paper/2604.12253/) (2026)
- [From Prohibition to Adoption: How Hong Kong Universities Are Navigating ChatGPT in Academic Workflows](https://mlsanity.com/paper/2410.01695/) (2024)
- [AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models](https://mlsanity.com/paper/2501.02063/) (2025)

## Abstract (arXiv)

The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and ethical considerations. Positive reactions highlight some potential, such as collaborative creativity, increased access to education, and empowerment of trainers and trainees. However, negative reactions raise concerns about ethical complexities, balancing innovation and academic integrity, unequal access, and misinformation risks. Through a systematic survey and text-mining-based analysis of global and national directives, insights from independent research, and eighty university-level guidelines, this study provides a nuanced understanding of the opportunities and challenges posed by GAI and LLMs in education. It emphasizes the importance of balanced approaches that harness the benefits of these technologies while addressing ethical considerations and ensuring equitable access and educational outcomes. The paper concludes with recommendations for fostering responsible innovation and ethical practices to guide the integration of GAI and LLMs in academia.