SBIR Phase I: Solving Minority Equity in Science, Technology, Engineering, and Mathematics (STEM) with Artificial Intelligence (AI)-Driven Workforce Development

Shamrck Social Impact Corp, Augusta GA

Investigators

Abstract

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to enhance Science, Technology, Engineering and Mathematics (STEM) career awareness in minorities by engaging their interests and correlating those interests with real career aspirations. The research aligns with diversity, equity, and inclusion goals in STEM fields, exposing students in secondary schools and providing support for career exploration that may need to be more equitably available to students. With the help of machine learning and artificial intelligence, the research is designed to evaluate the social impact involved in the misalignment of minorities in STEM fields and use technology to correct the alignment for larger, more prepared talent pools. This practice should help increase diversity in fields like biological sciences, data science, and engineering, to name a few. Additionally, upskilling talent before they enter the workforce helps to create a more robust workforce that can push the boundaries of STEM fields much faster, leading to new innovations, socioeconomic balance, and societal growth. This SBIR Phase I project combines the use of Holland occupational themes with natural language processing in machine learning to recommend and create pathways for secondary school students to gain knowledge and experience in anticipated career paths, especially STEM careers. Based on statistical data about underperforming schools and the workforce of an area, the system can nudge students through pathways to help them be more employable as well as to provide school resources more equitably. By using real-time data to train the models, students are able to gain career readiness skills and immediately apply those skills to complete project-based internships with small to medium businesses. This process ensures that information provided by the model is industry relevant and can pivot quickly to align with changes in an industry such as hiring patterns, technology, industrial-organizational psychology, and other trends that increase the applicability of a talent pool. 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.