This case study introduces the STARS (Supporting Talented African American Undergraduates for Retention and Success) project, designed to foster the retention and success of academically talented African American computer science students from low-income backgrounds at Historically Black Colleges and Universities (HBCUs) in the U.S. The STARS program employs a holistic approach, integrating four primary pillars of support: academic, social, career, and financial. Specific support provided includes near-peer mentoring, technical skill development seminars, undergraduate research, and high school outreach activities. To explore the program’s effectiveness and areas of improvement, a mixed-method evaluation study was conducted, collecting data through surveys, observations, individual interviews, and focus group interviews. The findings revealed that the STARS program contributed to high levels of retention among its scholars, and the mentoring program provided valuable networking opportunities. The study suggests that the program’s comprehensive approach, tailored to scholars’ needs, and combined with a culturally affirming learning environment, facilitates the retention and success of talented African American students in computer science.

The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills.

Despite growing emphasis on AI literacy education, empirical research examining how secondary students engage with and apply AI concepts in authentic project-based learning environments remains limited, particularly among students from historically underserved communities. This mixed-methods study examined African American high school students’ experiences in an AI-enhanced mobile app design project in which generative AI served as a collaborative design partner. Grounded in AI literacy frameworks, the program combined foundational AI instruction with a four-month project-based experience focused on designing AI-enhanced mobile applications. Qualitative data included focus group interviews, student design artifacts, presentations, and researcher field notes, while quantitative data included pre- and post-AI literacy assessments and surveys. Analysis of student design artifacts and presentations revealed that participants demonstrated emerging understanding of AI concepts by proposing AI-enabled features such as recommendation systems, chatbots, and information verification tools within their mobile app designs. Survey results indicated improvements in professional skills, particularly in collaboration, communication, and problem-solving. Knowledge assessment results showed modest but non-significant changes in students’ conceptual AI knowledge, with variation in individual learning trajectories. The study also identified several instructional challenges, including differences in students’ technological readiness and tensions surrounding AI use, ranging from overreliance to avoidance of AI use due to concerns about academic penalties. These findings highlight the potential of AI-enhanced project-based learning to support students’ development and application of AI literacy while underscoring the need for instructional scaffolding to guide students’ effective and ethical use of AI as a collaborative partner in K-12 learning environments.