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.
By Maxwell J. Jacobson, Gustavo Rodriguez-Rivera, Petros Drineas, Yexiang Xue
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.
By Xanthi Kokkinou, Chaido Mizeli, Nafsika Koulaxidou, Marina Delianidi, Konstantinos Diamantaras
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.
By Teri Rumble, Javad Zarrin, P. George Lovell, Ruth Falconer
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.
By Chang Nie, Zhe Liu, Hesheng Wang
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. That prior work validated LEA on a single STEM course (CMP511) exclusively through simulation, using synthetic learner agents.
arXiv:2609. 29672v1 Announce Type: new Abstract: Artificial intelligence helps education most where an essential provision has been rationed by cost.
By Qiming Guo, Jinwen Tang, Xingran Huang, Hung-Yu Lin, Yafu Zhong, Xiatian Zhuang
MIT Schwarzman College of Computing launched a pilot program that hosted a weeklong summer workshop for higher education faculty. The workshop focused on exploring how AI and machine learning materials can be adapted for use in their classrooms across various disciplines.
By Amanda Diehl | MIT Schwarzman College of Computing
arXiv:2606. 12428v1 Announce Type: cross Abstract: We present a report on the status of undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026.
By Felix Muzny, Carolyn Jones, Carter Ithier, Hasnain Sikora, Hrutika Harshadbhai Patel, Carla E. Brodley
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
By Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu, Linxi "Jim" Fan, Guanzhi Wang
OpenAI partners with the American Federation of Teachers to launch a 5-year initiative equipping 400,000 K-12 educators to lead AI innovation in classrooms.