WIP: Diversifying the Computing Workforce - Rapid Development of Interdisciplinary Computing-Based Undergraduate Programs

This innovative practice WIP paper considers the remarkable growth of Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) in recent years. This surge has sparked an urgent need for skilled workers in these highly-related areas, subject to growing calls for a diverse, inclusive technology workforce. This WIP paper presents an innovative approach in the rapid design and development of interdisciplinary undergraduate degree programs that are computing-based, with heavy emphasis in AI, ML, and DS while providing foundations for applications in specific domains. Located at the center of Silicon Valley, the Department of Computer Science (CS) at SJSU (San Jose State University) has been constantly bombarded by the speedy changes of technology and the corresponding need for skilled tech workers, and more recently, calls for a diverse tech workforce that matches the demographic of our community. As both an HSI (Hispanic Serving Institution) and AANAPISI (Asian American and Native American Pacific Islander Serving Institution), we understand that underrepresented groups face significant challenges. Prevailing stereotypes about computer scientists tend to reinforce the notion that women and minority do not have a place in this field. Trying to break through these stereotypes, we have reached out and develop interdisciplinary programs with majors that traditionally attract more women and minorities (such as majors in biology or the humanities). This paper first describes the success of two existing interdis-ciplinary, joint master degree programs: MS in Bioinformatics and MS in Data Science. Then, it illustrates how we rapidly developed a computing-based BS in Data Science (in one year), and concurrently a BS in Computer Science and Linguistics (in another year) that is jointly offered with the Department of Linguistics and Language Development (LLD). The paper presents the curriculum development approaches, ethical and social awareness components of the curricula, and uniqueness and success factors. Preliminary data show a significant increase in enrollment of women and low-income students in the first major core course of BS-DS. We believe that this paper will serve as a model initiative for rapidly developing a diverse technology workforce that meets the needs of emerging AI, ML and DS industries.