Full STEAM Ahead: Mentor-Mentee Matching Using AI for Underrepresented Professionals to Advance Diversity, Equity, and Inclusion in the Geosciences and Social Sciences

Traditionally, African Americans, Hispanic Americans, Native Americans, and women are exceedingly underrepresented within the geosciences fields. Although there have been a number of initiatives and programs that have sought to increase diversity and inclusion within geosciences from primary to higher education, these demographics remain largely underrepresented. As of now, geosciences have ranked as one of the least diverse STEM (science, technology, engineering, mathematics) fields. Yet, diversity continues to demonstrate multiple benefits for scientific advancement and innovation and address the complex challenges that we tackle in the geosciences. Full STEAM Ahead is a collaborative effort between Howard University and the National Oceanic and Atmospheric Administration (NOAA) to recruit mentors from across several different sectors in the geoscience/social sciences (e.g., government agencies, academe, industry, and non-governmental organizations) and pair them with the next generation of geoscience leaders using an AI-based matching algorithm in order to build effective mentoring relationships.

According to (West and Allen, 2018), Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision-making. Furthermore, artificial intelligence has the ability to make difficult and complex work more efficient, increase success ratios, and discover unexplored nuances (Khanzode and Sarode, 2020). The purpose of this project was to introduce an innovative and cutting-edge approach to provide the best mentor pairing to advance diversity, equity, and inclusion in these particular fields. Our project used a mentoring software program that has the capability to help identify and connect underrepresented mentees with mentors using an AI algorithm based on profiles and other participants’ submitted data. The software also provides other helpful features such as key performance indicators, activity measurements between mentor and mentees, milestones, and goal completions. The program provides an easy and cost-effective way to get participants meaningfully connected while being miles apart. This presentation will review the effectiveness of integrating an AI component regarding mentor pairing and offer future recommendations to advance diversity, equity, and inclusion in the Geoscience and Social Science fields.