arXiv AI

ELEVATE: Designing Human-Centered GenAI Virtual Tutors for Scalable and Inclusive Education

arXiv:2606. 30662v1 Announce Type: cross Abstract: The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while intensifying ethical debate about its adoption.

arXiv AI
Jun 16

Lect\=uraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching

arXiv:2606. 16428v1 Announce Type: cross Abstract: Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners.

By Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi, Guangyao Chen, B\"orje F. Karlsson
arXiv AI
Jun 2

Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation

arXiv:2602. 11790v2 Announce Type: replace Abstract: Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict logical rigor and precise knowledge representation, such as instructional and educational media.

By Lingyong Yan, Jiulong Wu, Dong Xie, Weixian Shi, Deguo Xia, Jizhou Huang
arXiv AI
6d ago

Empowering Children to Create AI-Enabled Augmented Reality Experiences

arXiv:2508. 08467v2 Announce Type: replace-cross Abstract: Despite their potential to enhance children's learning experiences, AI-enabled AR technologies are predominantly used in ways that position children as consumers rather than creators.

By Lei Zhang, Shuyao Zhou, Amna Liaqat, Tinney Mak, Brian Berengard, Emily Qian, Andr\'es Monroy-Hern\'andez
arXiv Machine Learning
Aug 4

Qwen-CUA: Native Computer Use for (almost) Everything

arXiv:2608. 02352v1 Announce Type: new Abstract: Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking, large-scale interactive experience, and learning from sparse yet verifiable outcomes.

By Dunjie Lu, Shuai Bai, Tianyi Bai, Sicheng Fan, Chang Gao, Jian Guan, Feng Hu, Mianqiu Huang, Xingyang Huang, Yizhen Jiang, Yuheng Jing, Dehui Kong, Ning Li, Dayiheng Liu, Shixuan Liu, Zheng Liu, Que Shen, Bowen Wang, Junli Wang, Chencan Wu, Rui Xie, Tianbao Xie, Zhihui Xie, Haiyang Xu, An Yang, Tao Yu, Wenzhen Yuan, Xi Zhang, Zhenru Zhang, Mingkang Zhu, Zhaoqing Zhu, Yizhong Cao, Kai Dang, Binyuan Hui, Kaixin Li, Junyang Lin, Haiquan Wang, Zekun Wang, Yiheng Xu, Fan Yan, Mengqi Yuan, Danyang Zhang, Jiajun Zhang, Zhipeng Zhang, Fan Zhou, Fan Zhou