VLDB 2026 Research / reviewers in the wild / expert
Clara DiMarco
dblp:407/7916
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › AI education
AI literacy |
1.0 | 1 | 2026 | AI Scholars Program: Scaling AI Literacy Through K-12 Outreach · AAAI 2026 |
Computing education
broadening participation in computing |
1.0 | 1 | 2026 | AI Scholars Program: Scaling AI Literacy Through K-12 Outreach · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
pre-post surveys · 1.0interviews · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | AI Scholars Program: Scaling AI Literacy Through K-12 OutreachabstractAs 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. Xiaoyi Tian 0001, Yasitha Rajapaksha, Ally Limke, Clara DiMarco, Emily Bryans Dobar, Marnie Hill, Jamie Payton, Tiffany Barnes |
AAAI | 4 |