SBIR Phase I: Artificial Intelligence (AI)-powered platform for evaluating and developing cultural competence and diversity, equity and inclusion awareness

Vsorts, Inc, Media PA

Investigators

Abstract

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project focuses on developing artificial intelligence (AI) agents for learners across the lifespan with an emphasis on those from pre-kindergarten (pre-K) to graduate school who access AI for support in educational spaces. An organization's or individual's capacity and effectiveness in engaging with other individuals is essential for success in classroom learning and in careers, and the proposed innovation seeks to create a new market for social science-inspired AI agents in the software development to serve learners across educational settings. The project’s impact on upskilling and advancement of all learners is an essential aspect of lifelong success, but often not an aspect included in all workspaces or learning spaces. The success of the proposed technology innovation will be a training program utilizing the latest advances in machine learning technologies to increase user access, satisfaction and trust in AI technology thereby driving innovation and generating significant revenue streams for organizations and companies while preparing individuals to take on essential roles in emerging fields and in an evolving marketplace. The traditional routes for developing technical competence in emerging technology domains such as artificial intelligence (AI) through instructor-led interventions can disrupt operational flow and be difficult to measure precisely, often limiting scalability in skill development. This SBIR Phase I project aims to design adaptive, agentic AI systems with dynamic learning analytics to accelerate workforce readiness and optimize human-AI collaboration. Leveraging principles from cognitive science, machine learning, and human-computer interaction, the project will create autonomous AI tutors capable of delivering precision feedback, real-time skill gap analysis, and performance-driven adaptive content generation. Key objectives include sourcing large-scale multimodal datasets to fine-tune AI agents for scenario-based training, reinforcement learning from human feedback (RLHF), and predictive modeling of learner progression. Phase I will transition a proof-of-concept into a modular, cloud-native platform with integrated AI orchestration, enabling scalable, precision-targeted training solutions for the educational marketplace and preparing a workforce equipped to thrive in automation-rich and AI-augmented 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.