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1. How do Computing Students Conceptualize Cybersecurity? Survey Results and Strategies for Curricular Integration

   https://doi.org/10.1145/3626252.3630869  

   Cowit, Noah Q; Ojha, Vidushi; Fiesler, Casey (March 2024, ACM)

   Cybersecurity expertise continues to be relevant as a means to confront threats and maintain vital infrastructure in our increasingly digitized world. Public and private initiatives have prioritized building a robust and qualified cybersecurity workforce, requiring student buy-in. However, cybersecurity education typically remains siloed even within computer and information technology (CIT) curriculum. This paper's goal is to support endeavors and strategies of outreach to encourage interest in cybersecurity. To this end, we conducted a survey of 126 CIT students to investigate student perceptions of cybersecurity and its major crosscutting concepts (CCs). The survey also investigates the prevalence of preconceptions of cybersecurity that may encourage or dissuade participation of people from groups underrepresented in computing. Regardless of prior learning, we found that students perceive cybersecurity as a relatively important topic in CIT. We found student perspectives on conceptual foundations of cybersecurity were significantly different (p < .05) than when simply asked about "cybersecurity," indicating many students don't have an accurate internal construct of the field. Several previously studied preconceptions of cybersecurity were reported by participants, with one misconception - that cybersecurity "requires advanced math skills" - significantly more prevalent in women than men (p < .05). Based on our findings, we recommend promoting cybersecurity among post-secondary students by incorporating elements of cybersecurity into non-cybersecurity CIT courses, informed by pedagogical strategies previously used for other topics in responsible computing. 

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2. Student Preconceptions of Artificial Intelligence: Results from Single Institution Survey

   https://doi.org/10.1145/3626253.3635484  

   Cowit, Noah Q; Fiesler, Casey (March 2024, ACM)

   Artificial intelligence (AI) has become an increasingly critical component of not only the computing workforce but also society. It is essential for a diverse group of young people to contribute to this field. However, even within computing, AI is not taught to all post-secondary students. Students often must self-select into AI courses, meaning their reasons for choosing AI may be based on preconceptions of the discipline that may or may not be accurate. We extend the work of a small-n interview study of primarily Asian/Asian American undergraduate students, many of whom expressed perceptions of AI that paralleled identified computing stereotypes. Many of these stereotypes have the potential to discourage undergraduate computing students to take classes or specialize in AI, particularly those from underrepresented groups. Here we present a larger scale validation of those findings in the form of survey data conducted at a large public research institution in the USA. The survey largely confirmed the findings of the interview study at a larger scale, and we also found that gender did not significantly influence the results. Finally, we discuss strategies for AI integration into non-AI computing courses based on those previously used in responsible computing contexts, the goal being to counter harmful preconceptions before students specialize into computing subareas. 

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3. Impact of Social and Programmatic Experiences on Students’ Interest in Pursuing a Graduate Degree in a Computing Field

   Maral Kargarmoakhar, Stephanie Jill (July 2021, 2021 ASEE Annual Conference)

   null (Ed.)

   There is a substantial shortage of students pursuing graduate degrees in computing fields in the United States [1], and when examining participation rates of minoritized populations the disparity is even greater [2]. In order to attract more domestic students to graduate schools in computing it is important to understand what factors encourage or discourage them from participating. Literature suggests that students’ family, friends, school, and society play an important role in their educational paths and self-perceptions. Using social impact theory as the guiding lens, we explored support from family and friends, as well as social and program-related experiences, in this study to assess their impact on undergraduate students' reported interest in pursuing a graduate degree. The research questions that guided this study are 1) Which social and programmatic experiences have the greatest impact on students’ interest in pursuing a graduate degree in computing?; and 2) How does a student’s gender/racial/ethnic background and their participation in social and programmatic experiences impact students’ interest in pursuing graduate degrees? We answered these research questions using data from a survey conducted at three large public universities in Florida which targeted students in computing fields (n=740). Data was analyzed using Kruskal-Wallis and Wilcoxon rank sum tests, as well as logistic regression. The findings revealed that “presenting work to other students,” and “research experience” are two experiences which lead to an increase of students’ interest in pursuing a graduate degree in a computing field. This study also demonstrated the importance of having same gender friends and reported interest in pursuing a graduate degree in a computing field. These findings provide insight into which experiences may impact domestic students' interest in pursuing graduate programs in computing fields. The results of this study are beneficial for universities to consider what factors may encourage more students to pursue a future in academia or in the workforce after obtaining a graduate degree. 

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4. Impact of Social and Programmatic Experiences on Students’ Interest in Pursuing a Graduate Degree in a Computing Field

   Kargarmoakhar, M. (June 2021, ASEE annual conference)

   null (Ed.)

   There is a substantial shortage of students pursuing graduate degrees in computing fields in the United States [1], and when examining participation rates of minoritized populations the disparity is even greater [2]. In order to attract more domestic students to graduate schools in computing it is important to understand what factors encourage or discourage them from participating. Literature suggests that students’ family, friends, school, and society play an important role in their educational paths and self-perceptions. Using social impact theory as the guiding lens, we explored support from family and friends, as well as social and program-related experiences, in this study to assess their impact on undergraduate students' reported interest in pursuing a graduate degree. The research questions that guided this study are 1) Which social and programmatic experiences have the greatest impact on students’ interest in pursuing a graduate degree in computing?; and 2) How does a student’s gender/racial/ethnic background and their participation in social and programmatic experiences impact students’ interest in pursuing graduate degrees? We answered these research questions using data from a survey conducted at three large public universities in Florida which targeted students in computing fields (n=740). Data was analyzed using Kruskal-Wallis and Wilcoxon rank sum tests, as well as logistic regression. The findings revealed that “presenting work to other students,” and “research experience” are two experiences which lead to an increase of students’ interest in pursuing a graduate degree in a computing field. This study also demonstrated the importance of having same gender friends and reported interest in pursuing a graduate degree in a computing field. These findings provide insight into which experiences may impact domestic students' interest in pursuing graduate programs in computing fields. The results of this study are beneficial for universities to consider what factors may encourage more students to pursue a future in academia or in the workforce after obtaining a graduate degree. 

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5. User Profiling in Human-AI Design: An Empirical Case Study of Anchoring Bias, Individual Differences, and AI Attitudes

   https://doi.org/10.1609/hcomp.v12i1.31608  

   Nourani, Mahsan; Hashky, Amal; Ragan, Eric D (October 2024, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   People form perceptions and interpretations of AI through external sources prior to their interaction with new technology. For example, shared anecdotes and media stories influence prior beliefs that may or may not accurately represent the true nature of AI systems. We hypothesize people's prior perceptions and beliefs will affect human-AI interactions and usage behaviors when using new applications. This paper presents a user experiment to explore the interplay between user's pre-existing beliefs about AI technology, individual differences, and previously established sources of cognitive bias from first impressions with an interactive AI application. We employed questionnaire measures as features to categorize users into profiles based on their prior beliefs and attitudes about technology. In addition, participants were assigned to one of two controlled conditions designed to evoke either positive or negative first impressions during an AI-assisted judgment task using an interactive application. The experiment and results provide empirical evidence that profiling users by surveying them on their prior beliefs and differences can be a beneficial approach for bias (and/or unanticipated usage) mitigation instead of seeking one-size-fits-all solutions. 

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