SBIR Phase I: Education and Training for an AI Integrated Future: Mixed-Reality, Competency Based Learning
- Institution
- MIXTA RE, INC, CT
- Principal investigator
- Travis Falstad
- Funding
- $304K
- Duration
- 2025
Moonbase score
Still being scored
Abstract
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 certification requirements, establishing new benchmark for evidence-based, adaptive learning in mixed reality environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Travis FalstadPI
- Jeanine A Defalco
LOADING MOONBASE SCORE
Questions for Mixta RE, Inc
Mixta RE, Inc hasn't set up its Moonbase portfolio yet, so questions about this research can't be asked here.
Related grants
Same topic, scored first.
- NSF · University of FloridaNot yet scored
Adaptive Contextual Learning in Microelectronics through Immersive Virtual Reality
Hyo Kang · $683K · 2025–2028
- NSF · University of California-Santa CruzNot yet scored
CNS Core: Small: Core Scheduling Techniques and Programming Abstractions for Scalable Serverless Edge Computing Engine
Liting Hu · $600K · 2024–2026
- NSF · University of South FloridaNot yet scored
Collaborative Research: Using Adaptive Lessons to Enhance Motivation, Cognitive Engagement, And Achievement Through Equitable Classroom Preparation
Autar K Kaw · $383K · 2024–2027