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.

Block-based programming puzzle games are popular as engaging, visual tools for introducing novice learners to programming. Many incorporate 3D environments, where players navigate and solve spatial challenges. However, little research has examined how spatial reasoning skills interact with game features to shape player experience. This study investigates the interplay between game features, spatial ability, in-game performance, and perceived difficulty within BOTs: 3D programming game where players plan spatial movements to solve puzzles. In an online study with 60 players, we examined how feature changes affected performance and perceived difficulty. Spatial skills strongly predicted performance but did not predict perceived difficulty. Larger or more complex layouts increased performance costs, with backward-facing player characters producing the largest spike in performance demands. Loops reliably increased perceived difficulty. Our findings highlight concrete needs for early spatial scaffolds, clearer support for mental-model shifts, and better cues for recognizing repetition and abstraction.