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

TL;DR

This paper surveys global academic guidelines on generative AI and LLMs, analyzing opportunities, ethical challenges, and providing recommendations for responsible integration in education.

Contribution

It offers a comprehensive analysis of international and national directives, highlighting best practices and ethical considerations for integrating GAI and LLMs in academia.

Findings

Diverse global guidelines reflect varying ethical priorities.

Identified key opportunities like enhanced access and creativity.

Highlighted challenges including misinformation and ethical concerns.

Abstract

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.