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<p>Artificial Intelligence (AI)–powered tools, particularly Large Language Models (LLMs), are increasingly being explored as catalysts for educational transformation. While their application in urban and higher education contexts has gained traction, there remains a significant gap in understanding how such technologies can support K–12 learning in under-resourced rural environments. This study investigates the perceptions of student volunteer educators on the integration of LLMs in rural Indian classrooms. Drawing from 23 semi-structured interviews conducted with volunteers engaged in teaching initiatives across Rajasthan and Delhi, we employed Braun and Clarke’s thematic analysis framework to extract key themes related to AI readiness, digital infrastructure, pedagogical challenges, and community attitudes. The findings reveal a complex landscape: volunteers recognize the potential of LLMs to personalize learning, alleviate teacher workload, and provide round-the-clock academic support. However, adoption is constrained by infrastructural limitations, lack of AI literacy among teachers, language barriers, and parental skepticism. Concerns regarding student over-reliance on AI, data privacy, and ethical risks also emerged. Despite these challenges, participants emphasized that AI should serve as a supportive augmentation rather than a replacement for human-led instruction. The study highlights the need for localized implementation strategies, culturally relevant content, and structured training programs to ensure equitable and responsible integration of GenAI tools in rural schools. By foregrounding the lived experiences of volunteer teachers situated between technologically advanced urban settings and underserved rural realities, this research contributes grounded insights into the design, deployment, and policy planning of AI-based learning interventions in the Global South.</p>

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Thematic insights into the impact of large language models on K-12 education in rural India from student volunteers’ perspectives

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月28日

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  • Harshita Goyal,
  • Garima Garg,
  • Prisha Mordia,
  • Veena Ramachandran,
  • Dhruv Kumar,
  • Jagat Sesh Challa

摘要

Artificial Intelligence (AI)–powered tools, particularly Large Language Models (LLMs), are increasingly being explored as catalysts for educational transformation. While their application in urban and higher education contexts has gained traction, there remains a significant gap in understanding how such technologies can support K–12 learning in under-resourced rural environments. This study investigates the perceptions of student volunteer educators on the integration of LLMs in rural Indian classrooms. Drawing from 23 semi-structured interviews conducted with volunteers engaged in teaching initiatives across Rajasthan and Delhi, we employed Braun and Clarke’s thematic analysis framework to extract key themes related to AI readiness, digital infrastructure, pedagogical challenges, and community attitudes. The findings reveal a complex landscape: volunteers recognize the potential of LLMs to personalize learning, alleviate teacher workload, and provide round-the-clock academic support. However, adoption is constrained by infrastructural limitations, lack of AI literacy among teachers, language barriers, and parental skepticism. Concerns regarding student over-reliance on AI, data privacy, and ethical risks also emerged. Despite these challenges, participants emphasized that AI should serve as a supportive augmentation rather than a replacement for human-led instruction. The study highlights the need for localized implementation strategies, culturally relevant content, and structured training programs to ensure equitable and responsible integration of GenAI tools in rural schools. By foregrounding the lived experiences of volunteer teachers situated between technologically advanced urban settings and underserved rural realities, this research contributes grounded insights into the design, deployment, and policy planning of AI-based learning interventions in the Global South.