Recruiting, retaining, and educating students in computing is a frequent research topic in CHI. However, students’ sociotechnical experiences of registering for classes are understudied—especially those of socioeconomic-diverse students. These experiences matter: research shows that registration problems bring long-term consequences to student successes. We investigate students’ socioeconomic status (SES) impact on registration experiences through three studies: a case study with education professionals using an emerging analytic method, SocioeconomicMag (SESMag); interviews with faculty/staff/students from 8 universities; and observations of 14 SES-diverse students registering for classes. Results showed: (1) 5 SES-inclusivity bugs which arose 30 times, 72% more often by lower-SES students than by higher-SES students. (2) 6/7 lower-SES students (but only 2/7 higher-SES students) expected downstream problems from the registration issues. (3) The risk-to-negative-outcomes rate was 3 times higher for lower-SES students. (4) The issues generalized across 8 universities and potentially to >700 other universities who use the same registration portal.

This work-in-progress (WIP) innovative practice paper focuses on skills developed by student instructional team members working in peer instruction in computing. Research has shown that peer instruction models such as supplemental instruction (SI) support computer science student learning and peer community building while extending the reach of the instructional team. SI participants achieve higher grades and experience greater retention rates in the STEM majors than their non-participating peers, an impact that can extend beyond the initial supplemental instruction semester. While benefits for Supplemental Instruction learners are well documented, the program's effect on the leaders is not well documented. Supplemental Instruction leaders receive training, mentoring, and peer guidance in developing teaching practices that meet students' needs. Unlike lecture-based instruction, supplemental instruction at Kean University involves modeling, guided problem-solving, and interaction with learners in small groups. Students who attend supplemental instruction may arrive with specific questions or academic needs that must be addressed to support further learning of the subject matter, making SI sessions spaces for improvisation and adaptive teaching. The authors contend that leading supplemental instruction sessions for computer science first and second-semester coursework allows leaders to develop flexible communication practices and career adaptability skills, which are vital” soft skills” for career development. Research indicates that career adaptability leads to stronger employment outcomes, such as wages, job satisfaction, and workplace engagement. This work-in-progress paper draws on career development, entrepreneurial mindset research, and social science research on improvisation. The paper makes inferences regarding the apprenticeship and guidance practices that support adaptive thinking and improvisation in SI at Kean University, and develops recommendations for faculty and staff who lead peer instruction efforts in engineering and/or computing departments. Innovations in this paper include considering how SI leaders develop career adaptability in their work, which can support leader recruitment and lead to new mechanisms for improving SI leader performance.

Motivation: Many university CS programs have begun teaching various types of CS-related societal issues using approaches such as ethics, Responsible CS, inclusive design, and more. However, some recent research suggests that, although these programs have been able to teach awareness, students often fail to act upon this awareness. To address this problem, University X's CS program tried an unusual approach—integrating hands-on inclusive design skills in small ways across all four years of the CS major. But did it work? That is, did the students who experienced this change across the major actually build more inclusive technology than the students who did not experience it?

This paper addresses the significant challenge of selecting suitable foundation shades, particularly for darker skin tones, which have been inadequately represented and catered to in the beauty industry. It introduces an innovative system designed to offer individualized makeup recommendations, significantly diminishing the time and effort traditionally demanded of consumers when searching for appropriate products. Utilizing machine learning methodologies and computer vision techniques, the system analyzes image data to precisely identify a range of skin tones, enabling it to propose compatible foundation shades automatically. Unlike sophisticated Large Language Models, such as ChatGPT, Gemini, Microsoft Copilot, or Claude, which are not equipped to undertake such visually driven tasks due to ethical guidelines that prohibit them from processing personal images without explicit consent and the absence of face image processing capabilities, this technological development represents a step forward in fostering inclusivity, illustrating the transformative potential of AI in accommodating the unique beauty preferences of all individuals.

Computer science (CS) and information technology (IT) curricula are grounded in theoretical and technical skills. Topics like equity and inclusive design are rarely found in mainstream student studies. This results in graduates with outdated practices and limitations in software development. A research project was conducted to educate the faculty to integrate inclusive software design into the CS undergraduate curriculum. The objective is to produce graduates with the ability to develop inclusive software.