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While a lucrative and growing field, low levels of gender and racial diversity in Computer Science (CS) remain prevalent. Education and workforce support programs with the intention to promote underrepresented students' persistence in CS exist, which teach CS skills, inform of career options, and grow students' network in CS. Studies have demonstrated these programs' effectiveness as it relates to changes in affective outcomes, such as participants' confidence in CS skills and attitudes towards CS jobs. However, the longitudinal impact of CS support programs on participants' build-up of social capital in CS, and the resulting social capital's influence on their persistence in the field, remain unexplored. Motivated by the literature that associates demographic identifiers with access to social capital, and students' access to developmental relationships and career-related resources (social capital) in CS with their persistence, this study explores a CS support program's impact on persistence through capital building. We focus on Google's Computer Science Summer Institute (CSSI), which provided graduating high school students with a 3-week-long introduction to CS. We use interviews with participants who are now 2-5 years out of the program to study CSSI's impact on their social capital and long-term persistence in CS. Thematic analysis reveals three features of the program that influenced students' build-up of social capital, and that the resulting persistence was realized through students' progress towards internships in CS and goals for paying-it-forward in CS. These findings inform our recommendations that future CS support programs and educational settings consider mentorship centered on socioemotional support, opportunities for collaboration, and time for fun social activities. Additional suggestions center on engaging socially-oriented individuals with CS support programs. These insights inform facilitators and educators in CS on design choices that can encourage the persistence of underrepresented students in CS. [ABSTRACT FROM AUTHOR]

The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We conduct an exploratory study to gain an initial understanding of persistence in this field and identify fruitful directions for future work. One factor that has been shown to predict persistence in engineering is belonging; we study belonging through the lens of confidence, and discuss how attention to social belonging confidence may help to increase diversity in the profession. In this research paper, we conduct a small set of interviews with students in ML/AI courses. Thematic analysis of these interviews revealed initial differences in how students see a career in ML/AI, which diverge based on interest and programming confidence. We identified how exposure and initiation, the interpretation of ML and AI field boundaries, and beliefs of the skills required to succeed may influence students' intentions to persist. We discuss differences in how students describe being motivated by social belonging and the importance of close mentorship. We motivate further persistence research in ML/AI with particular focus on social belonging and close mentorship, the role of intersectional identity, and introductory ML/AI courses. [ABSTRACT FROM AUTHOR]

The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We conduct an exploratory study to gain an initial understanding of persistence in this field and identify fruitful directions for future work. One factor that has been shown to predict persistence in engineering is belonging; we study belonging through the lens of confidence, and discuss how attention to social belonging confidence may help to increase diversity in the profession. In this research paper, we conduct a small set of interviews with students in ML/AI courses. Thematic analysis of these interviews revealed initial differences in how students see a career in ML/AI, which diverge based on interest and programming confidence. We identified how exposure and initiation, the interpretation of ML and AI field boundaries, and beliefs of the skills required to succeed may influence students' intentions to persist. We discuss differences in how students describe being motivated by social belonging and the importance of close mentorship. We motivate further persistence research in ML/AI with particular focus on social belonging and close mentorship, the role of intersectional identity, and introductory ML/AI courses. [ABSTRACT FROM AUTHOR]

Machine Learning and Artificial Intelligence are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms, recidivism algorithms, and resume reviewing algorithms, when they are designed without diversity in mind. As Machine Learning (ML) and Artificial Intelligence (AI) expand into more areas of our lives, we must take action to promote diversity among those working in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. While the persistence of diverse populations has been studied in engineering specifically, and Science, Technology, Engineering and Math (STEM) more generally, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI in order to identify areas for improvement. We surveyed undergraduate and graduate students enrolled in ML/AI courses at a major North American university in fall 2021. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from existing studies of persistence in STEM, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field. Our findings suggest that short-term intentional persistence in ML/AI is associated with academic enrollment factors such as major and level of study. In terms of long-term intentional persistence, we found that measures of professional role confidence developed to study persistence in engineering are also important predictors of intent to remain in ML/AI. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence in ML/AI, and women generally care more about this. We find some evidence that having high confidence in non-technical interpersonal skills may also be a positive predictor of long-term intentional persistence. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field. [ABSTRACT FROM AUTHOR]