Artificial intelligence (AI) represents the future of the workforce, but existing curricula inadequately prepare students to comprehend and use these new technologies. Despite the push for educators to teach AI literacy, there is a distinct lack of research exploring student perspectives on the topic. Utilizing an explanatory sequential mixed methods research design, we first administered an AI literacy survey to undergraduate students in a computing major to learn how they think about AI, and then conducted focus group interviews after further refining our research questions. There was a discrepancy between undergraduate competence with AI applications and underlying AI principles, which were conflated on the survey and positively influenced overall knowledge. Participant confidence in AI's capability as a learning tool was infrequently limited by perception of personal ability, but rather by beliefs about limitations in AI tool efficacy. Participants believed that students pursuing any field would benefit from AI literacy and that AI literacy education, if implemented effectively, could mitigate concerns with AI pervasion in the workplace. A combination of surveys and assessments will be beneficial when centering students in AI curricula, the former establishing a student's AI confidence and the latter competence.

Computing and technological innovation directly influence how people live, work, and learn. As undergraduate computer science majors move into professional spaces, it is critical that they understand the relationship between their work and its impact on people and communities. One way to encourage the development of socially conscious computer scientists is to engage students in computer science learning that integrates social science theory and concepts. In this Birds of a Feather session, we will discuss how to integrate ideas from the social sciences into computer science research and classroom settings, thereby providing undergraduates with hands-on, human-centered research experiences. We will begin the workshop by analyzing the overlap between the social sciences and computer science. Then, we will share personal examples of how social lenses have been integrated into traditional computing research and academic environments and lean on our experiences to develop personalized action plans to integrate social science theory and concepts into computing research efforts. Attendees will leave this session with a stronger understanding of how social science and computing research overlap and practical strategies to integrate social science theory into their existing research practices.

The demand for computing talent is at an all-time high, but not everyone feels equally welcome or has equal access to pursue opportunities in technology fields. Women, non-binary, Black, Hispanic/Latino/a/x, and first-generation students are the least likely to have access to paid undergraduate internships that lead to job opportunities in technology companies. This experience report discusses two programs designed to address the lack of internships among historically excluded populations: a micro-internship program and a career program. We present data showing the role that each program played in creating professional pathways for racially diverse undergraduate women and non-binary students. Both programs prepare these undergraduates for professional environments by exposing them to tech careers and developing their social capital through professional networking. Quantitative and qualitative data were collected as well as summer employment plans to evaluate the effectiveness of the program. We present the short- and long-term impact of each program on students' career interests and sense of belonging in computing. A summary of lessons learned from implementing each program are also shared for stakeholders interested in designing similar programs.

As society increasingly relies on computers to drive social, economic, and political decisions, the computing workforce must reflect the racial and gender diversity of the larger population. This experience report presents CompSciConnect (CSC), a multi-year program designed to broaden participation in computing for middle school aged students from historically excluded populations (girls and/or Black, Latina/o/e/x, and Native American [BLNA] students). Each cohort of CSC participants span three years with participants meeting for 2 weeks in the summer and one weekend a month during the school year. Students progress through three levels of the program: Yellow (beginner), Red (intermediate), and Terp (advanced). Quantitative and qualitative data guided the growth and implementation of CSC. CSC began in 2012 with just 14 students and now has reached over 532 students. Additionally, CSC alumni cite their experiences in CSC as contributing to their decisions to major in a computing field. Various design factors-such as community-centered student recruitment strategies, the long-term structure of the program, and the scaffolded curriculum-contributed to CSC's growth and its positive impact on CSC participants. In this paper, we present CSC, elaborate on the design factors that led to CSC's success, and highlight the challenges and lessons learned throughout CSC's development.

In response to the persistent diversity gap that exists in the computing field, we implemented a five-day professional development (PD) workshop for elementary teachers on integrating computational thinking (CT) and culturally responsive teaching (CRT) practices into their existing science instruction. An explicit focus of this PD was to engage teachers in analyzing CT- and CRT-integrated science classroom scenarios (i.e., vignettes). Our analyses of video and written data of teachers' vignette responses indicate that while their understanding of CT increased as a result of the PD, the teachers need additional support around CRT. We suggest that future studies explore how teachers define and enact CRT, especially within their unique contexts.

The benefits of near-peer mentoring models in computing education contexts are well known, but research on near-peer mentor training best practices is limited. This research evaluates the impact of a 3-week instructor training on undergraduate CS majors' confidence and preparedness to teach and lead K-12 informal computing programs across a 12-week summer period. This poster presents features of the training that were beneficial to near-peer undergraduate instructors as they performed their summer teaching duties and factors that could be improved or further developed in the future.

This poster reports on undergraduate computer science (CS) majors' experiences as near-peer instructional leaders in informal K-12 computing programs during a 12-week summer period. We analyze how the undergraduates' prior experiences in computing and their intersectional computing identities influence their self-perceptions as mentors and instructors. Case study analyses of the undergraduates revealed that their journeys in computing (e.g., their level of access to computing education courses or their exposure to influential role models) directly influenced their mentor and instructor identity development. This poster provides evidence for the need for more intersectional approaches to near-peer mentoring models.