Development of Interdisciplinary Computing-Based Programs for Democratizing Computing Workforce

Publication Date

1-1-2026

Document Type

Conference Proceeding

Publication Title

Communications in Computer and Information Science

Volume

2939 CCIS

DOI

10.1007/978-3-032-22202-2_4

First Page

42

Last Page

55

Abstract

This paper considers the fast growth of Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) in recent years, which has sparked an urgent need for skilled workers in these highly-related areas, subject to the growing calls for a diverse, inclusive technology workforce. This paper presents an innovative approach to rapidly designing and developing interdisciplinary, computing-focused degree programs emphasizing AI, ML, and DS, while providing foundations for domain-specific applications. Prevailing stereotypes about the computer workforce often suggest that women and minorities lack the ability to succeed in the field. To challenge these stereotypes, we have reached out and developed interdisciplinary programs with majors that traditionally attract more women and minorities. This paper outlines how we quickly developed a computing-based BS in Data Science within one year, followed by the concurrent creation of a BS and MS in Computer Science and Linguistics with the Department of Linguistics the next year, then a BS and MS in Computer Science and Geology with the Department of Geology in the subsequent year, and the ongoing development efforts with the Department of Biology. The paper outlines the approaches to curricular development, incorporates ethical and social-awareness elements, and highlights the unique aspects and success factors. Preliminary data show an increase in enrollment of women in these programs. We believe that this paper will serve as a model initiative for rapidly developing a diverse technology workforce everywhere that meets the needs of emerging AI, ML and DS industries.

Funding Sponsor

Northeastern University

Keywords

artificial intelligence, Curriculum development, data science, diversity, joint degree programs, machine learning

Department

Computer Science

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