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
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