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Agentic University: Collaborative Agents for Personalized Learning· Agentic University: 个性化学习的协作智能体

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

AI 解读论文

通过协作智能体实现个性化学习的系统

核心方法
提出了一种人类参与的多智能体组织模型,并在Agentic University中实现,学习者作为组织成员与专门的智能体合作,共同维护可修订的能力计划
适合谁读
研究者、教育技术开发者、教育工作者
要解决的问题
如何将大型语言模型的支持与学习者的目标、活动和评估对齐,形成连贯且可进化的学习过程
关键实验
通过对Agentic University的评估,展示了该系统相较于传统学习方法的优势
主要贡献
提供了一个框架,连接学习者的个人目标、多模态学习活动和多层次评估,使学习过程更具个性化和适应性
意义与局限
有助于推动个性化教育技术的发展,但需要进一步验证其长期效果和广泛适用性
领域:cs.AI作者:Jingru Fan、Tianle Zhou、Yunjie Zhou
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