Background
Jharkhand's PALASH Mother Tongue-Based Multilingual Education (MTB-MLE) programme has demonstrated measurable improvements in foundational literacy among tribal children. However,scaling the programme is severely bottlenecked by a shortage of teachers proficient in tribal languages including Ho, Mundari, and Santhali -languages with limited digital NLP resources. The vast majority of teachers assigned to tribal-area primary schools are Hindi-medium trained and lack the linguistic tools to deliver mother-tongue-based instruction. Without a technology bridge, the pedagogical intent of MTB-MLE cannot be realised at scale, and children in over 5,000 tribal-area primary schools continue to receive instruction in a language they do not comprehend at home.
Description
Develop an Al-assisted translation and curriculum-generation software suite that enables non-nativespeaking primary school teachers to deliver mother-tongue-based instruction in Ho, Mundari, and Santhali without prior language training. The system must include an NLP engine capable of translating standard Hindi Foundational Literacy and Numeracy (FLN) curriculum content- including lesson scripts, activity instructions, and assessment prompts-into contextually accurate text and synthesised audio in target tribal languages. A real-time voice-to-voice translation feature must allow a teacher speaking Hindi to conduct interactive classroom dialogue with tribal-language-speaking students, with latency not exceeding three seconds. The system must auto-generate bilingual worksheets and visual flashcard sets aligned to the NIPUN Bharat learning outcomes framework.Given that most schools in the target deployment areas lack reliable internet, the entire application must function offline on low-cost tablets (?2 GB RAM, Android 9+) after initial content synchronisation.
Expected Solution
A working software application demonstrating Hindi-to-tribal-language translation (minimum one tribal language at prototype stage), real-time voice translation with sub-3-second latency, autogenerated bilingual worksheet output, and full offline operation on a low-end Android tablet submitted with a demo video - and GitHub repository.