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This paper introduces a 'hubs-based' framework to scale K-12 AI and robotics education, especially in rural areas, by training undergraduate mentors.

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Teaching AI, Robotics, & Community: A Hubs-Based K-12 Education Framework for Reaching Rural Schools

Maxwell J. Jacobson, Gustavo Rodriguez-Rivera, Petros Drineas, Yexiang Xue

In plain terms

Teaching AI and robotics to K-12 students, particularly in rural areas, is difficult due to a lack of sustained expert mentorship. To address this, the researchers developed a framework called AI, Robotics, & Community (ARC). In this model, colleges act as central 'hubs' that train their undergraduate students to become mentors. These mentors then host workshops and support K-12 robotics teams in nearby schools, with mature school programs potentially becoming secondary hubs themselves. A trial deployment showed significant increases in K-12 students' programming knowledge and resource access, along with increased confidence in teaching for undergraduate mentors. Through simulations, the ARC framework is projected to reach a large percentage of schools and create numerous robotics programs over time, significantly outperforming natural growth.

Why it matters · For newcomers, this paper highlights practical challenges in scaling AI education and offers a structural solution, demonstrating how educational frameworks can leverage existing resources to bridge the digital divide. It showcases a blend of social organization and educational impact, important for real-world application of AI education.

About this work · This research explores innovative models for expanding access to STEM education, specifically in AI and robotics, focusing on equitable outreach to underserved communities. It fits into the broader field of educational frameworks designed to scale specialized technical skills and promote AI literacy.

K-12 educationAI literacySTEM educationMentorship modelsRural education
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