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The paper introduces ARC, a hubs‑based framework that trains undergraduate mentors at colleges to run workshops for nearby K‑12 robotics teams, allowing mature programs to become secondary hubs and expand mentorship reach. A trial at one university created three rural teams, showing significant gains in students’ programming knowledge, resource access, and practice opportunities, as well as increased confidence among undergraduate mentors. A spatial Markov model predicts that, under moderate conditions, ARC could reach 74% of Indiana’s public K‑12 schools and generate 992 robotics programs in 40 years, far exceeding natural growth projections.
arXiv:2607. 14046v1 Announce Type: new Abstract: This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation.
This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation. It aims to enhance earthquake preparedness and conscious action among primary-school students.
arXiv:2607. 13370v1 Announce Type: cross Abstract: This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes.
OpenAI Academy is expanding its offerings with new learning paths tailored for employees, developers, leaders, educators, and students. These paths aim to help participants build and demonstrate practical AI skills. The initiative broadens access to AI education across diverse audiences.
Teach-and-Grow Learning (TGL) is an agent-centered architecture that transforms a few successful demonstrations into reusable Skill Blocks, enabling a robot to compose, execute, and revise behaviors in new scenes without task-specific policy retraining. The system maintains a Skill Library and structured Experience Memory to capture successes, failures, and repairs, allowing persistent reuse and agent-directed adaptation. Evaluation on the LIBERO benchmark shows state-of-the-art performance, and the authors propose a scaling-law hypothesis suggesting that accumulated reusable experience reduces future-task error and teaching demand following a power-law trend.