Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions

TL;DR

This paper proposes using Small Language Models as offline, AI-powered educational tools to improve physics and photonics learning in underdeveloped regions, addressing infrastructure and resource limitations.

Contribution

It introduces the application of Small Language Models as scalable, offline virtual tutors to enhance STEM education in resource-scarce areas.

Findings

SLMs can operate offline on low-power devices.

SLMs enable native-language instruction and interactive learning.

Potential to reduce educational inequities in STEM.

Abstract

Limited infrastructure, scarce educational resources, and unreliable internet access often hinder physics and photonics education in underdeveloped regions. These barriers create deep inequities in Science, Technology, Engineering, and Mathematics (STEM) education. This article explores how Small Language Models (SLMs)-compact, AI-powered tools that can run offline on low-power devices, offering a scalable solution. By acting as virtual tutors, enabling native-language instruction, and supporting interactive learning, SLMs can help address the shortage of trained educators and laboratory access. By narrowing the digital divide through targeted investment in AI technologies, SLMs present a scalable and inclusive solution to advance STEM education and foster scientific empowerment in marginalized communities.