Trustworthiness of Legal Considerations for the Use of LLMs in Education

Different regions prioritize different trustworthiness principles: The paper finds that while the EU emphasizes transparency and data privacy through strict regulations, the US focuses more on innovation with lighter oversight, and GCC countries are developing frameworks that balance AI advancement with local cultural values—highlighting that there is no one-size-fits-all approach to responsible AI in education.

Legal frameworks for educational AI are still evolving globally: The analysis reveals significant gaps in how existing AI regulations address education-specific concerns, suggesting that policymakers need tailored guidance beyond general AI governance to ensure LLMs are deployed safely and fairly in schools and universities.

A practical governance checklist can help educators and developers comply with multiple standards: The paper introduces a tiered compliance framework and institutional checklist designed to help schools, regulators, and AI developers navigate complex, sometimes conflicting international and local requirements—making responsible AI adoption more feasible in practice.

Accountability and human oversight remain critical but underspecified: Across all regions studied, the paper identifies that while accountability and human oversight are recognized as essential trustworthiness principles, most regulations lack clear mechanisms for who is responsible when LLMs cause harm in educational settings.

Rapid AI adoption in education outpaces legal and ethical safeguards: The paper emphasizes that LLMs are being integrated into classrooms faster than regulatory frameworks can keep pace, particularly in emerging markets, creating urgent need for proactive governance to prevent harms related to bias, privacy, and educational equity.