Trustworthiness of Legal Considerations for the Use of LLMs in Education

2025-08-05

AI Summary

This study addresses ethical and regulatory challenges in deploying large language models (LLMs) for education globally, focusing on divergent governance approaches across the EU, UK, US, China, and the Gulf Cooperation Council (GCC). Using comparative policy analysis, legal text interpretation, and governance framework design, it examines how transparency, fairness, accountability, data privacy, and human oversight—core trustworthy AI principles—are institutionalized within these five regulatory regimes. The analysis identifies critical normative gaps and cultural adaptation challenges confronting GCC states as they accelerate national AI strategies and educational innovation. The study innovatively proposes a compliance-oriented AI education governance framework tailored to the GCC, featuring tiered classification standards and institutional audit checklists that harmonize international ethical guidelines with local sociocultural values. The resulting actionable toolkit supports regional AI education governance innovation, facilitates cross-jurisdictional regulatory alignment, and advances culturally responsive AI system development.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

As Artificial Intelligence (AI), particularly Large Language Models (LLMs), becomes increasingly embedded in education systems worldwide, ensuring their ethical, legal, and contextually appropriate deployment has become a critical policy concern. This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions, including the European Union, United Kingdom, United States, China, and Gulf Cooperation Council (GCC) countries. It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures. Special emphasis is placed on the evolving landscape in the GCC, where countries are rapidly advancing national AI strategies and education-sector innovation. To support this development, the paper introduces a Compliance-Centered AI Governance Framework tailored to the GCC context. This includes a tiered typology and institutional checklist designed to help regulators, educators, and developers align AI adoption with both international norms and local values. By synthesizing global best practices with region-specific challenges, the paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education. These insights are intended to inform future regulatory harmonization and promote responsible AI integration across diverse educational environments.

Problem

• Ensuring ethical and legal deployment of LLMs in education globally
• Comparing AI regulatory frameworks across EU, UK, US, China, and GCC
• Developing a GCC-specific AI governance framework for education

Innovation

• Comparative analysis of global AI regulatory frameworks
• Compliance-Centered AI Governance Framework for GCC
• Tiered typology and institutional checklist for AI adoption