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 investigates how Large Language Models (LLMs) could influence rural K-12 education in India by eliciting grounded perspectives from 23 student volunteers across Rajasthan and Delhi. Using semi-structured interviews and Braun & Clarke thematic analysis, the authors identify opportunities for personalized learning and workload reduction alongside barriers such as poor connectivity, insufficient AI training, language constraints, and parental skepticism. Key contributions include context-aware insights that frame AI as a supplementary tool rather than a replacement, and a concrete pathway toward NCERT-based, regionally localized AI solutions (including an offline-capable mobile app) designed to ensure accessibility and equity. The findings have practical implications for policy, NGO programs, and AI tool design, highlighting the need for blended learning models, targeted AI literacy, and robust data governance to responsibly harness AI for rural education. Overall, the work provides a grounded, actionable roadmap for deploying LLMs in resource-constrained rural settings to help narrow the rural-urban education divide while preserving essential human-centric pedagogy.

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.

Figures (1)

• Figure 1: Research Methodology Flowchart