As artificial intelligence (AI) becomes increasingly integrated into daily life, there is a critical need for developing AI literacy across all educational levels. However, current AI education remains largely confined to college-level computer science classrooms with limited access for K-12 learners. We present the AI Scholars Program, a novel approach that addresses the AI education gap by preparing college computing students to serve as AI education ambassadors in their communities and empowering K-12 teachers to adopt AI education practices in their classrooms. This experience report presents the curriculum and its outcomes after one round of refinement. The program offers structured AI learning through bi-weekly webinars, resources, and collaborative opportunities to form teams and conduct community outreach projects. Our program invited 63 scholars from 30 institutions across the U.S., including 51 college students and 12 K-12 teachers. Their outreach impacted over 230 K-12 learners. We examine program outcomes for participants and projects through pre/post surveys measuring computing attitudes and self-efficacy for teaching AI, scholar interviews, and outreach project reports. We share lessons learned and challenges for designing similar programs, highlighting the importance of involving educators for effective community-engaged AI education. The program creates a sustainable pipeline for college students to develop technical skills and leadership while addressing K-12 AI education shortages. We contribute insights for scaling AI literacy and broadening participation in computing.

Educators have long claimed that pedagogical content knowledge (PCK), ways of presenting a subject that make it comprehensible to others, is a critical element of student academic success. This paper presents an exploratory study finding that PCK significantly correlates with students' computer science (CS) content knowledge acquisition while teacher CS content knowledge does not. This study analyzes systemic factors comprising the theory of change for the EarSketch learning intervention including classroom- and student-level factors that contribute to shifts in students' attitudes toward computing and ultimately to students' intentions to persist in future computing endeavors and students' CS content knowledge. We present the results of a multi-level modeling analysis and offer an exploratory approach to measuring CS PCK along with recommendations to improve the sensitivity of the method.

Educators have long claimed that pedagogical content knowledge (PCK), ways of presenting a subject that make it comprehensible to others, is a critical element of student academic success. This poster presents findings from a preliminary study of Computer Science Principles teachers that demonstrate a significant correlation between teacher PCK and student content knowledge. The study describes the measures used to assess PCK, operationalizes the mechanisms for measuring teacher pedagogical content knowledge, shares the results from a multi-level modeling analysis, and identifies methodological improvements for follow-up research. The findings from this initial study show correlations of teacher PCK with student content knowledge while accounting for other teacher- and student-level variables across multiple schools.