Adaptive NLP for Vernacular Education

Abstract: This paper presents the design, implementation, and validation of an adaptive Natural Language Processing (NLP) pipeline engineered to translate and adapt STEM (Science, Technology, Engineering, and Mathematics) educational content from English into Indian vernacular languages, with a primary implementation for Hind. The system addresses critical linguistic barriers in multilingual educational landscapes by integrating domain-aware machine translation, adaptive content simplification, and specialized Optical Character Recognition (OCR) for Indic scripts. Experimental validation with Hindi-medium learners demonstrates significant improvements in comprehension metrics and learner engagement compared to the use of English source materials or generic translation tools. The proposed modular architecture is designed for extensibility to other regional languages, presenting a scalable solution for promoting equitable access to quality STEM education.

Keywords: Natural Language Processing, STEM Education, Machine Translation, Vernacular Languages, Adaptive Learning, OCR, Educational Technology