SBIR Phase I: A platform to connect underserved and underrepresented communities to science, technology, engineering and mathemetics (STEM) careers

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in providing more access to educational pathways, and equitable opportunities for learning, professional development, and career growth to marginalized student communities. The project proposes the development and implementation of a digital human element to meet the needs of these impacted individuals.

The project is not such organizations, but also to create pathways to equitable opportunities for those coming from marginalized communities to participate in some of the most innovative learning modalities, while studying for some of the most promising STEM careers.

The intellectual merit of this project lies in the use of big data and machine learning in personality and skills measurement. The artificial intelligence algorithms will be juxtaposed onto game mechanics to facilitate ease of use due to the familiarity with other existing user interfaces. The interface has a complex and dynamic personality profiling engine.

The research will deliver standards and methodologies, evaluate existing exchange formats, improve accuracy metrics for neural networks, and deliver an initial digital human prototype. The technology will create a Data Lake containing professions, skills, certificate requirements, social media profiles, resumes, recorded interviews, and other online activities that are shared by the users for establishing personalized, artificial intelligence (AI)-supported career growth profiles.

Information in the Data Lake will be curated to facilitate the development of personalized career development strategies. A Delta Lake model will be used to continuously stream data with improved data quality to drive the personalization requirements of both the digital human and the user.

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.