This research explores how Small Language Models (SLMs) can democratize AI by making machine intelligence more accessible, affordable, and efficient for everyday tasks.

• SLMs represent a practical alternative to large models for deployment on personal devices
• They prioritize efficiency and accessibility over the pursuit of artificial general intelligence
• Research aims to bridge the attention gap between cloud-based LLMs and device-friendly SLMs
• Particularly relevant for educational applications where cost and accessibility are critical barriers

For education stakeholders, this research highlights pathways to implement AI-powered learning tools without requiring expensive cloud infrastructure, potentially enabling more equitable access to AI-enhanced learning.

Small Language Models: Survey, Measurements, and Insights