Agentic University supports personalized learning with collaborative agents
Agentic University: Collaborative Agents for Personalized Learning
Artificial IntelligenceComputation and Language
Summary
Learning can be more effective when teaching adapts to what each person needs, but making this work smoothly is hard. The authors present Agentic University, a system where learners team up with software agents that help plan learning goals, activities, and tests, and adjust this plan based on how the learner is doing. This setup helps learners improve more and feel better supported than traditional methods. The system keeps the learner involved in deciding the learning path and uses feedback to guide what comes next.
What this means in practice
- •For e-learning platform developers: Build adaptive learning platforms that dynamically revise personalized study plans using multiple specialized agents coordinating learner goals and assessments.$Commercial implications: Enables creation of personalized tutoring software with enhanced learner engagement and improved assessment outcomes, attractive for education technology products.
- •For corporate training teams: Design employee training programs that incorporate agent-supported personalized pathways adapting to trainees’ progress and feedback in real time.
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{https://github.com/AgenticOrgLab/AgenticUniversity.git}.