Small Language Models in Educational Contexts: Applications, Trends, and Future Implications

Abstract

Small Language Models (SLMs), typically ranging from hundreds of millions to several billion parameters, emerging as transformative tools in educational settings. Unlike their larger counterparts, SLMs offer distinct advantages including enhanced privacy preservation, reduced computational requirements, and cost-effective deployment on consumer-grade hardware. This paper examines the current landscape of SLM applications across diverse educational domains including health and medical education, programming education, mathematics education, science education, language instruction, and financial literacy. Drawing from recent research and implementations, we analyze the technical approaches employed, key advantages realized, and challenges encountered in deploying SLMs for educational purposes. Our analysis reveals that when properly fine-tuned and augmented with domain-specific knowledge through techniques such as Retrieval-Augmented Generation (RAG), SLMs can achieve performance comparable to large language models while maintaining significantly lower resource requirements. We identify critical future directions including the need for standardized evaluation frameworks, improved reasoning capabilities, and scalable infrastructure solutions. This paper contributes to the growing discourse on democratizing AI in education by highlighting how SLMs can provide accessible, privacy-preserving, and pedagogically effective educational support on a scale.