We partnered as senior technical advisors on an AI initiative focused on expanding language translation capabilities for low-resourced languages. The effort sought to address a profound global challenge: more than 250 million children without access to formal schooling, and another 600 million struggling with basic literacy and numeracy.

The project’s vision was an AI-powered platform capable of transforming national curriculum standards into personalized, culturally relevant lesson plans—delivered in native languages through a mobile-first interface. This required balancing cutting-edge research with practical deployment in some of the world’s most resource-constrained environments.

Our contributions centered on designing the technical roadmap for natural language processing, guiding architectural decisions, and assessing feasibility across domains such as zero-shot learning, transfer learning, and synthetic data generation. We advised on the structural complexities of machine translation in under-resourced languages, where minimal digital data existed, while ensuring the platform supported both mobile delivery and offline use.

We also played a key role in developing a successful National Science Foundation Small Business Innovation Research (SBIR) grant proposal. By translating complex technical goals into measurable milestones and aligning them with societal impact frameworks, we helped secure funding for a defined proof-of-concept path. Our work included advising on technical risk mitigation, data strategy planning, and cross-functional alignment to ensure long-term viability.

The result was a funded initiative positioned to scale globally, bridging advanced machine learning with urgent educational equity needs. This project laid the groundwork for AI systems capable of generating curriculum content in underserved languages—helping close the educational gap for communities historically overlooked by mainstream edtech.