The Impact of Large Language Models on K-12 Education in Rural India: A Thematic Analysis of Student Volunteer's Perspectives

TL;DR

This study explores rural Indian volunteer teachers' perceptions of AI, especially Large Language Models, highlighting challenges like connectivity and literacy gaps, while emphasizing AI's potential to support personalized learning and reduce workload.

Contribution

It provides novel insights into rural educators' views on AI adoption, focusing on infrastructure, training needs, and ethical considerations in a previously under-researched context.

Findings

LLMs can enhance personalized learning in rural classrooms.

Connectivity and digital literacy are major barriers to AI adoption.

Teachers see AI as a supportive tool, not a replacement.

Abstract

AI-driven education, particularly Large Language Models (LLMs), has the potential to address learning disparities in rural K-12 schools. However, research on AI adoption in rural India remains limited, with existing studies focusing primarily on urban settings. This study examines the perceptions of volunteer teachers on AI integration in rural education, identifying key challenges and opportunities. Through semi-structured interviews with 23 volunteer educators in Rajasthan and Delhi, we conducted a thematic analysis to explore infrastructure constraints, teacher preparedness, and digital literacy gaps. Findings indicate that while LLMs could enhance personalized learning and reduce teacher workload, barriers such as poor connectivity, lack of AI training, and parental skepticism hinder adoption. Despite concerns over over-reliance and ethical risks, volunteers emphasize that AI should be seen as a complementary tool rather than a replacement for traditional teaching. Given the potential benefits, LLM-based tutors merit further exploration in rural classrooms, with structured implementation and localized adaptations to ensure accessibility and equity.