TY - GEN
T1 - Computing Specializations
T2 - 54th ACM Technical Symposium on Computer Science Education, SIGCSE 2023
AU - Ojha, Vidushi
AU - Perdriau, Christopher
AU - Lagesse, Brent
AU - Lewis, Colleen M.
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/3/2
Y1 - 2023/3/2
N2 - Artificial intelligence (AI) and cybersecurity are in-demand skills, but little is known about what factors influence computer science (CS) undergraduate students' decisions on whether to specialize in AI or cybersecurity and how these factors may differ between populations. In this study, we interviewed undergraduate CS majors about their perceptions of AI and cybersecurity. Qualitative analyses of these interviews show that students have narrow beliefs about what kind of work AI and cybersecurity entail, the kinds of people who work in these fields, and the potential societal impact AI and cybersecurity may have. Specifically, students tended to believe that all work in AI requires math and training models, while cybersecurity consists of low-level programming; that innately smart people work in both fields; that working in AI comes with ethical concerns; and that cybersecurity skills are important in contemporary society. Some of these perceptions reinforce existing stereotypes about computing and may disproportionately affect the participation of students from groups historically underrepresented in computing. Our key contribution is identifying beliefs that students expressed about AI and cybersecurity that may affect their interest in pursuing the two fields and may, therefore, inform efforts to expand students' views of AI and cybersecurity. Expanding student perceptions of AI and cybersecurity may help correct misconceptions and challenge narrow definitions, which in turn can encourage participation in these fields from all students.
AB - Artificial intelligence (AI) and cybersecurity are in-demand skills, but little is known about what factors influence computer science (CS) undergraduate students' decisions on whether to specialize in AI or cybersecurity and how these factors may differ between populations. In this study, we interviewed undergraduate CS majors about their perceptions of AI and cybersecurity. Qualitative analyses of these interviews show that students have narrow beliefs about what kind of work AI and cybersecurity entail, the kinds of people who work in these fields, and the potential societal impact AI and cybersecurity may have. Specifically, students tended to believe that all work in AI requires math and training models, while cybersecurity consists of low-level programming; that innately smart people work in both fields; that working in AI comes with ethical concerns; and that cybersecurity skills are important in contemporary society. Some of these perceptions reinforce existing stereotypes about computing and may disproportionately affect the participation of students from groups historically underrepresented in computing. Our key contribution is identifying beliefs that students expressed about AI and cybersecurity that may affect their interest in pursuing the two fields and may, therefore, inform efforts to expand students' views of AI and cybersecurity. Expanding student perceptions of AI and cybersecurity may help correct misconceptions and challenge narrow definitions, which in turn can encourage participation in these fields from all students.
KW - artificial intelligence
KW - broadening participation in computing
KW - computer science education
KW - cybersecurity
UR - http://www.scopus.com/inward/record.url?scp=85149891326&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85149891326&partnerID=8YFLogxK
U2 - 10.1145/3545945.3569782
DO - 10.1145/3545945.3569782
M3 - Conference contribution
AN - SCOPUS:85149891326
T3 - SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education
SP - 966
EP - 972
BT - SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education
PB - Association for Computing Machinery
Y2 - 15 March 2023 through 18 March 2023
ER -