arXiv cs.AI· Jingru Fan, Tianle Zhou, Yunjie Zhou, Zihao He, Yixuan Zhou, Kezhao Wu, Xinyu Zheng, Xiyuan Wang, Yuheng Wang, Zhi Li, Songhua Liu, Weinan E, Chen Qian·· 2 小时前AI 评分32
Agentic University：用于个性化学习的多智能体协作框架
Agentic University: Collaborative Agents for Personalized Learning
AI 导读
研究者提出"人类参与式多智能体组织"，并落地为 Agentic University（AU）：学习者作为组织成员与专业化智能体协作，共同维护一份可修订的 Capability Program，将个人目标、跨模态学习活动与分层评估连接起来。评测显示用户平均评估提升达 12.19 分（对照组 5.61 分，50 分制），并在项目组织、评估设计和修订质量上表现更优。
正文 · 原文
Authors:Jingru Fan, Tianle Zhou, Yunjie Zhou, Zihao He, Yixuan Zhou, Kezhao Wu, Xinyu Zheng, Xiyuan Wang, Yuheng Wang, Zhi Li, Songhua Liu, Weinan E, Chen Qian
Abstract:Large language models support personalized tutoring, content generation, and adaptive learning paths. However, capability development requires these resources and interactions to form a coherent learning process that evolves with the learner. A central challenge is to align goals, activities, and assessment while using learning evidence to decide what should follow. We introduce Human-Participatory Multi-Agent Organization and instantiate it in Agentic University (AU). In AU, learners participate as organization members and work with specialized agents to maintain a revisable Capability Program. The program connects personal goals, learning activities across modalities, and layered assessment, while learner feedback and performance guide subsequent support. Our evaluation shows advantages in program organization, assessment design, and revision quality. Users achieved larger mean assessment gains (12.19 versus 5.61 points on a 50-point scale) and reported stronger cognitive support. This work contributes an organizational approach to adaptive learning that makes learner participation a continuing basis for multi-agent coordination. The Agentic University project is available at \href{this https URL}.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.40492 [cs.AI] |
| (or arXiv:2610.40492v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40492 arXiv-issued DOI via DataCite |
Submission history
From: Jingru Fan [view email]
[v1]
Thu, 29 Oct 2026 17:18:07 UTC (22,594 KB)
来源：arXiv cs.AI · arxiv.org