Legal gaps threaten safe use of generative AI in education

With the growing reliance on large language models (LLMs) in education, it has become urgent to align their use with legal compliance, data privacy, and cultural integrity. A new study submitted on arXiv takes on this complex terrain.

Titled “Trustworthiness of Legal Considerations for the Use of LLMs in Education,” the paper compares global regulatory responses while proposing a region-specific framework for AI governance in the Gulf Cooperation Council (GCC) countries. It provides a structured assessment of how the legal and ethical deployment of LLMs in educational contexts varies across major geopolitical regions, and how these differences shape institutional trust.

From data sovereignty to fairness and algorithmic transparency, the study highlights that global policy harmonization is still in its infancy. Most notably, it outlines a compliance-centered governance framework tailored to GCC nations, aiming to bridge the divide between international standards and regional socio-legal expectations.

How do global regions differ in governing educational AI?

The study maps the regulatory environments across five global blocs: the European Union, the United Kingdom, the United States, China, and the GCC. While each region acknowledges the risks of integrating AI into education, such as bias, data misuse, and lack of accountability, their approaches differ widely in terms of enforcement, oversight, and foundational trust principles.

In the European Union, AI regulation is framed within the context of the General Data Protection Regulation (GDPR) and the EU AI Act. These mechanisms prioritize data protection, explainability, and user rights. The United Kingdom mirrors many of these priorities but follows a more innovation-friendly, risk-based model that favors industry-led compliance.

In the United States, where regulation is fragmented across sectors and states, the focus has been on voluntary guidelines rather than binding legal frameworks. China’s model, on the other hand, is characterized by strong state-led AI development with stringent controls on data use and content generation. The researchers highlight that China’s central governance strategy provides operational efficiency but raises questions about transparency and user autonomy.

In contrast, GCC countries are only beginning to formalize their national AI strategies, with limited legal infrastructure in place. This absence of region-specific frameworks leaves educational institutions and developers without clear guidance. The authors stress that without legally binding, culturally attuned policies, the GCC's ambitious digital transformation goals may be undermined by legal uncertainty and public mistrust.

What legal and ethical gaps arise in educational use of LLMs?

Educational AI deployment raises unique legal questions not fully addressed by existing frameworks. LLMs introduce several complex risks: compromised student data privacy, intellectual property conflicts, algorithmic bias, and integrity concerns in grading and content delivery.

The authors highlight that LLMs often function as opaque systems. Their inability to justify outputs in human-understandable terms contradicts the principle of explainability, a cornerstone of trustworthy AI. In educational settings where decisions affect student performance, fairness and transparency become non-negotiable.

Moreover, the study draws attention to gaps in intellectual property law when generative models are used to create teaching materials, student essays, or academic assessments. Without clear legal protections, both educators and students may inadvertently breach copyright or ethical codes.

The risk of embedding cultural and linguistic bias into educational content generated by AI is especially pronounced in regions like the GCC, where local values, norms, and languages are underrepresented in global AI training datasets. The study stresses that these blind spots can lead to culturally inappropriate or misleading outputs, further eroding trust.

Additionally, the lack of regulatory standardization across borders raises compliance risks for transnational educational institutions operating in multiple jurisdictions. A learning platform acceptable under U.S. norms might violate privacy laws in the EU or contradict ethical principles in the GCC.

How does the proposed GCC framework address regulatory and cultural needs?

To mitigate these issues, the study introduces a Compliance-Centered AI Governance Framework designed specifically for GCC countries. This framework is structured around a tiered typology that categorizes AI applications in education based on risk, usage context, and decision-making impact. The typology helps institutions identify the appropriate level of legal scrutiny and ethical oversight required.

The framework also includes a practical institutional compliance checklist that guides developers, regulators, and educators through core trust principles: data privacy, transparency, fairness, human oversight, and cultural sensitivity. By embedding these elements early in the AI adoption lifecycle, institutions can avoid costly retrofitting and public backlash.

Notably, the framework promotes contextual alignment, ensuring that LLMs used in GCC education systems are compliant not just with global norms but also with Islamic legal traditions, linguistic diversity, and local governance structures. The authors propose periodic ethical audits, AI curriculum integration, and regulator-developer collaboration to maintain ongoing compliance.