A Pilot Study on the Impact of LLMs on Virtual Tutoring for Low- to Middle-Income Countries

Abstract

Large Language Models (LLMs) demonstrate increasing proficiency in solving complex tasks and explaining scientific concepts, positioning them as potential tools for democratizing access to personalized education. In low- and middle-income countries, students in rural regions often lack access to high-quality tutoring due to high costs and limited human resources, while the impact of LLMs for this setting is underexplored. To address this gap, we present a pilot study exploring the feasibility of LLMs as personalized tutors for Vietnamese K–12 students preparing for university entrance exams with 540 yes/no questions and focusing on three core STEM subjects: mathematics, physics, and chemistry. Preliminary results indicate both potential benefits and challenges of implementing LLM-driven tutoring systems in resource-constrained educational environments.