Trustworthiness of Legal Considerations for the Use of LLMs in Education

TL;DR

This paper analyzes global legal and ethical frameworks for deploying Large Language Models in education, emphasizing trustworthiness principles and proposing a tailored governance framework for the GCC region.

Contribution

It provides a comparative analysis of international AI regulations and introduces a compliance-centered governance framework specifically for the GCC educational context.

Findings

Mapping of core trustworthiness principles in regional legislation

Introduction of a tiered typology and institutional checklist

Guidance for aligning AI systems with international norms and local values

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.