Ainun Fatimah
Universitas Hasanuddin

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Natural Language Processing for Multilingual Education: Breaking Language Barriers Kamsinah Kamsinah; Ainun Fatimah; Nurasia Natsir
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.199

Abstract

Language barriers represent one of the most significant obstacles to educational equity and access worldwide. This study investigates the application of Natural Language Processing (NLP) technologies in multilingual educational contexts to facilitate cross-linguistic learning and improve educational outcomes for linguistically diverse student populations. We implemented and evaluated a comprehensive NLP-powered multilingual learning platform across 47 educational institutions in 12 countries, serving 8,450 students speaking 23 different languages. Our experimental framework integrated machine translation, speech recognition, multilingual content generation, and adaptive language learning algorithms. Results demonstrate that NLP-enhanced multilingual education improved student comprehension by 43.6% (p<0.001), increased participation rates by 67.8%, and reduced achievement gaps between native and non-native speakers by 52.4%. Students using NLP-assisted learning tools achieved test scores averaging 78.3% compared to 54.7% for control groups. However, challenges persist regarding cultural context preservation, idiomatic expression handling, and equitable performance across language families. This research provides evidence that NLP technologies can effectively democratize education across linguistic boundaries while identifying critical areas requiring continued development.