Multilingual instruction and Natural Language Processing in Indian higher education: A collaborative approach

Abstract

This study investigates how multilingual instruction (MLI) supported by Natural Language Processing (NLP) tools is perceived and used in Indian higher-education classroom. Particularly as the National Education Policy (NEP) 2020 in India emphasized the need of multilingual instruction in the classroom. However, the implementation of NLP in multilingual classrooms presents challenges, including language diversity, technological accessibility, and the need for culturally sensitive tools. Despite these limitations, the collaborative approach of combining multilingual instruction with NLP can revolutionize higher education by providing equal learning opportunities for all students, regardless of their linguistic backgrounds. 50 faculty members in Jaipur, India, were surveyed through questionnaire and class observations, which include six domains: (1) frequency of incorporating MLI in teaching, (2) challenge in MLI, (3) adoption of NLP tools in MLI, (4) effectiveness of NLP tools, (5) benefit observations from using NLP tools in MLI, and (6) challenge in using NLP tools in MLI. Figures display adoption rates, perceived benefits, and reported challenges. Findings indicate frequent use of MLI, growing but uneven NPL adoption, and perceived benefits in accessibility and participation; key barriers include translation accuracy, training, and infrastructure. Observations show how NLP features (live transcription/translation) facilitate participation, while accuracy issues sometimes disrupt flow. This study concludes with practical implications: targeted faculty development, infrastructure upgrades, and culturally sensitive NLP models. Future study should extend sampling beyond Jaipur and include student outcomes.