Company

SBIR Phase I: Education and Training for an AI Integrated Future: Mixed-Reality, Competency Based Learning

Amount: $304,200   Topic: LC

The broader/commercial impact of this SBIR Phase I project leverages adaptive and personalized mixed-reality training solutions to address the critical challenge of workforce displacement due to artificial intelligence (AI). AI and automation technologies will displace between 400 million and 800 million individuals globally by 2030, requiring up to 375 million workers to switch occupational categories and learn new skills. An adaptive learning platform will democratize access to high-quality, personalized training experiences, serving displaced workers, older persons, economically disadvantaged youth, and vocational learners. The platform accelerates the acquisition of essential competencies in AI literacy, data science, healthcare technical roles, and vocational skills through immersive, evidence-based learning experiences. This technology bridges the growing skills gap by validating existing competencies while developing new ones, enabling faster workforce transitions and creating new pathways to employment in an AI-integrated economy. This Small Business Innovation Research (SBIR) Phase I project develops a novel adaptive learning platform through three core technological innovations: a skills engine, an authoring tool, and an extended reality (XR) player. The skills engine uses Generative AI and RAG to identify and map key competencies, creating personalized learning pathways based on individual skill profiles. The authoring tool transforms the creation of adaptive learning content through AI-assisted scenario generation, automated asset creation, and integrated assessment tools, reducing development time and costs while maintaining high educational standards. The XR player delivers these experiences through augmented and virtual reality, adapting in real-time to learner performance and capturing analytics for competency validation. The platform architecture integrates these components seamlessly while maintaining compliance with IEEE standards and RAMP cer