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Despite the importance of AI literacy for both children and adults, adults have been understudied. We developed short videos for adults that provided training on the basics of AI understanding, use, and evaluation. In an online experiment, 94 adults aged 30-49 were randomly assigned in a 1:2 ratio to view either short videos on AI history (control group) or AI literacy training videos (treatment group). The results showed that the intervention significantly improved people’s self-efficacy of AI use but not in AI understanding or evaluation. Interestingly, participants’ fears of AI bias, privacy violations, and job replacement increased after the training, although they remained below the midpoints. We argue that the heightened fear in the treatment group reflects a foundation for critical thinking skills, as it moves them closer to a more calibrated, moderate level of fear. Therefore, this study uniquely contributes by utilizing short-form experiential content to both educate and foster a more informed, critical interaction with AI technologies. The implications of designing AI literacy educational materials for adults were discussed. 

As Artificial Intelligence (AI) continues to influence various aspects of society, the need for AI literacy education for K-12 students has grown. An increasing number of AI literacy studies aim to enhance students’ competencies in understanding, using, and critically evaluating AI systems. However, despite the vulnerabilities faced by students from underserved communities—due to factors such as socioeconomic status, gender, and race—these students remain underrepresented in existing research. To address this gap, this study focuses on leveraging the cultural capital that students acquire from their communities’ unique history and culture for AI literacy education. Education researchers have demonstrated that identifying and mobilizing cultural capital is an effective strategy for educating these populations. Through collaboration with 26 students from underserved communities—including those who are socioeconomically disadvantaged, female, or people of color—this paper identifies three types of cultural capital relevant to AI literacy education: 1) resistant capital, 2) communal capital, and 3) creative capital. The study also emphasizes that collaborative relationships between researchers and students are crucial for mobilizing cultural capital in AI literacy education research. 

HRI scholars envision a future of work where human-robot collaboration brings mutual gains: organizations benefit from increased efficiency and productivity, and laborers benefit when tasks are redistributed between humans and robots based on their respective strengths. Yet, ironically, this collaboration in real-world contexts can lead to the opposite effect-workers' efficiency may decrease due to the additional tasks they must undertake to manage unexpected errors caused by robots. This “stop-gap” labor, often viewed as temporary and naturally manageable over time, can have significant and persistent impacts on workers. Drawing from observations across multiple robot deployment sites, this paper highlights the overlooked burden of this labor, challenging idealized visions of seamless human-robot collaboration. We argue that attending to stop-gap labor presents an opportunity for the HRI community to make genuine improvements for workers as primary stakeholders within complex socio-economic networks.