Conceptualising local AI infrastructure using small language models (SLMs) for primary education

Introduction Recent advances in artificial intelligence (AI) have positioned large language models (LLMs) as potentially transformative tools for education. However, their adoption in primary schools raises significant concerns regarding student data privacy, institutional governance, and the affordability of AI infrastructure. Methods This paper presents a conceptual analysis of locally hosted small language models (SLMs) as an alternative to cloud-dependent AI systems in primary education. The study examines developmental, institutional, and regulatory factors relevant to primary school settings and proposes a school-based architecture integrating low-cost hardware and open-source software frameworks operating entirely within local school networks. Results The proposed architecture demonstrates how locally deployed SLMs in primary schools can support educational AI applications while reducing dependence on external cloud providers. By maintaining data processing and storage within school networks, the framework may strengthen data sovereignty, improve institutional control over AI operations, and enhance accessibility for resource-constrained schools through lower infrastructure costs. Discussion The findings suggest that, within primary education, institutional controllability, privacy protection, and equitable access may be more valuable objectives than maximising model capability. This conceptual framework contributes to the emerging literature on educational AI governance by outlining a sociotechnical pathway that prioritises child data protection, institutional autonomy, and practical deployment feasibility while supporting the responsible integration of AI into educational environments.