Company

SBIR Phase I: World of Hello: Using artificial intelligence (AI) for an interactive language-learning process designed for young children

Amount: $303,226   Topic: LC

The broader commercial impact of this SBIR Phase I project lies in its potential to revolutionize early speech development tools, addressing the critical need for accessible and personalized support for children with communication delays. This project aims to create an innovative platform that combines cutting-edge technology with user-friendly design to assist families and educators in fostering language growth. By tailoring learning activities to individual progress and incorporating adaptive learning models, this solution will empower caregivers to actively participate in their child's developmental journey, bridging the gap between therapy sessions and home practice. The anticipated commercial potential includes licensing to educational institutions and healthcare providers, as well as direct subscriptions for families, making the platform scalable and sustainable. In its third year, the platform is projected to serve over 500,000 users nationwide, improving outcomes for children and reducing the need for costly, resource-intensive interventions. This innovation aligns with NSF’s mission to advance national health and welfare by creating a durable, inclusive solution that enhances scientific understanding and promotes equitable access to educational resources. This Small Business Innovation Research (SBIR) Phase I project addresses the challenge of providing effective, scalable support for children with communication delays through advanced technology. The research aims to develop an adaptive AI-driven platform that personalizes language learning activities based on the phonetic and linguistic needs of each child. The core innovation lies in the integration of phoneme recognition algorithms and adaptive learning models to track, assess, and respond to language progress. The project will focus on developing a robust framework for speech pattern analysis using machine learning techniques, ensuring accurate and inclusive recognition across diverse linguistic