Artificial Intelligence Tutors in India’s Classrooms: A Comparative Exploration of Language Education through Adaptive Systems

Abstract

Artificial Intelligence tutors are rapidly emerging as transformative tools in India’s language education landscape, offering personalized learning pathways that can adjust to students’ needs in real time. This study investigates the effectiveness of adaptive AI-based tutoring systems across diverse Indian classroom settings, focusing on English and regional language learning. Using a comparative, mixed-method design, the research evaluates three widely implemented AI tutor models deployed in government, low-cost private, and premium urban schools. The analysis integrates system-level performance data, classroom observations, learning-analytics dashboards, and semi-structured interviews with teachers and students. Findings reveal that adaptive AI tutors significantly enhance vocabulary acquisition, pronunciation accuracy, and reading comprehension, but their impact varies depending on school type, digital infrastructure, and teacher-AI integration practices. Government schools displayed the strongest gains in reading fluency due to consistent AI scaffolding, while premium schools showed better outcomes in higher-order language skills because of richer multimodal inputs. The study concludes that AI tutors are not stand-alone replacements for human teaching; their success depends on pedagogical alignment, device accessibility, and sustained teacher involvement. The results highlight the urgent need for policy frameworks that ensure equitable AI deployment to bridge India’s widening digital-learning divide.