CISE-MSI: DP: IIS-III: ACOSUS: An AI-driven COunseling System for Underrepresented Transfer Students

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Statistics in 2021 show that only 15% of workers in computing occupations are Black and Hispanic, and 25% are female, whereas 32.56% of college students are underrepresented minorities. There is a dire need for a highly diversified workforce in computing-related fields. A crucial contributor to a diverse workforce is the transfer pathways between community colleges and 4-year universities. In fact, many of these institutions have challenges co-advising transfer students due to the uncertainty surrounding the path from their decision to transfer to post-transfer graduation, particularly those who are from disadvantaged minority or underrepresented groups in computing majors. The use of information systems to assist these students in their upskilling transfer to universities and job market success is a critical but challenging objective of higher education. The overarching goal of this project is to (1) investigate the challenges experienced by students in their transfer decision-making process, school performance, and job placement, and (2) design an innovative AI-driven student advising system that provides personalized readiness assessment and suggestions to underrepresented transfer students in computing majors, among others.The project aims to gain a comprehensive understanding on how personal, academic, and behavioral factors influence underrepresented students in their pursuit of education and career decisions. It also explores environmental factors like social norms and social media that impact their decision and goal-pursuing endeavors. Using this information, the project designs and evaluates an AI-driven advising system that integrates personal, academic, behavioral, and environmental factors specific to underrepresented students to not only assess their preparedness for upskilling transfer and the job market, but provide improvement suggestions and resources as well. The system uses natural language processing algorithms, deep neural networks, along with other AI approaches in its design. It also employs data curation techniques to eliminate the racial and gender bias that is prevalent in current AI-driven systems.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.