Bodhan AI, launched by IIT Madras, introduces four foundational AI models to serve as digital public infrastructure for India's education sector, with a focus on speech, translation, and text recognition across more than 22 Indian languages.

A Teacher Assistant Bot helps educators generate lesson plans, worksheets, quizzes, and revision material, while teachers retain control to review, edit, regenerate, or discard AI-generated content.

The initiative pursues collaboration over competition, aiming to build a common, nation-wide Edu AI layer rather than each entity building separate capabilities.

There are significant data gaps: much Indic data remains non-digitised or of low quality with poor metadata, and existing benchmarks often translate English data, reducing native-language nuance.

India's vast manuscript and inscription collections—palm-leaf, Grantha, Sharada, among others—hold valuable knowledge that requires collaboration among technologists, historians, linguists, and scholars to preserve context.

As a public-good research project, sustainability, ongoing funding, data privacy, anonymization, and long-term maintenance are critical to ensure accuracy and reliability across India's diverse education system.

A core challenge is building high-quality, machine-readable knowledge infrastructure in Indian languages, not just scaling models or compute.

OCR for Indian scripts is a critical bottleneck, with issues like mixed scripts, handwriting, and complex layouts hindering AI usability.

Governance and licensing emphasize publicly governed foundational knowledge layers and clear licensing to address copyright, consent, compensation, and cultural considerations.

The initiative prioritizes human oversight and regulatory compliance, focusing on curriculum-aligned support rather than instructional replacement, to enable sustainable, widespread adoption.

Long-term AI capability depends on robust, multilingual knowledge ecosystems; India’s future hinges on connecting existing blocks into a cohesive national system that preserves context and culture.

Policy proposals call for a national AI knowledge infrastructure, including a corpus authority, metadata standards, licensing frameworks, and a national OCR and multilingual data tooling effort.