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

2024-05-26

arXiv.org

AI Summary

This study addresses the tension between innovation incentives and ethical risks arising from integrating generative artificial intelligence (GAI) and large language models (LLMs) in education. Methodologically, it systematically collects and performs textual mining on AI usage guidelines issued by 80 universities and academic institutions worldwide, complemented by comparative policy analysis and qualitative content coding. It thereby constructs, for the first time, a cross-national, cross-institutional mapping of AI governance in higher education. The analysis identifies six core governance dimensions and three prototypical implementation patterns, leading to the proposal of a “balanced governance” framework that concurrently upholds academic integrity, equitable access, pedagogical innovation, and misinformation mitigation. The resulting actionable roadmap for responsible AI integration in education has already informed AI teaching policy development at multiple universities.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Technology governance, policy, and regulations
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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.

Problem

• Analyzing global academic guidelines for Generative AI and LLMs integration.
• Exploring pedagogical benefits and ethical challenges of GAI and LLMs in education.
• Recommending balanced approaches for responsible innovation and equitable access.

Innovation

• Systematic survey and text-mining analysis
• Balanced approaches for ethical integration
• Recommendations for responsible AI innovation