Disclosure-based cyberbullying is defined as sharing personal information without consent, often with malicious intentions. This affects young adults and leads to long-term personal damage (i.e., career, reputation, and family). Current strategies on social media platforms fall short of addressing this problem. We engaged 20 young adults (18-34) through a participatory design approach in co-designing solutions that reflect their concerns, needs, and desires. Participants were divided into three groups based on their cyberbullying experience as victim, discloser, or attacker. Our participants designed 15 solutions for prevention, intervention, support, and awareness. These solutions reveal the need for privacy control, anonymity, and autonomy for victims, a restorative justice system, social media accountability, and the involvement of influencers and content creators in awareness and education. We provide implications for progressive and adaptive social media awareness campaigns, a compassionate justice system, and soft control of personal information ownership.

With the pervasive and evolving use of tracking and AI to make inferences about online platform users, it has become imperative for adolescents—a key demographic using such platforms—to develop a deep understanding of these practices to protect their privacy. Traditionally, K-12 cybersecurity education has largely been confined to extracurricular activities, limiting underrepresented students’ access. To resolve this shortcoming, we partnered with a rural-identifying middle school to deliver AI-related privacy education in classrooms. Using Design-Based Research methodology, we identified students’ AI-related privacy learning needs and developed six education modules. This paper focuses on the design, classroom implementation, and evaluation of module #2, covering the privacy threats of Tracking and Pervasive Personalization (TaPP). Student assessment outcomes show they developed transferable foundational knowledge of the privacy implications of tracking and personalization after participating in the TaPP module. Our findings demonstrate the benefits of integrating AI-related privacy education into existing K-12 curricula.

Lack of diversity and high dropout rates among underrepresented students plague the CS discipline. We developed, administered, and validated survey scales measuring social factors that impact the retention and graduation of under-represented CS undergrads at two institutions. Results revealed significant differences between students who identify as men vs. women in terms of computing identity and confidence, and between black and non-black students in terms of familiarity with future opportunities.