arXiv 2510.132152026-02-06cs.AIcs.CL

基于目标驱动学习者状态建模的个性化学习路径规划

Personalized Learning Path Planning with Goal-Driven Learner State Modeling

Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology（计算机科学与技术系，信息科学国家研究中心）；Tsinghua University（清华大学）；Institution of Education（教育研究所）；Department of Statistics and Data Science（统计与数据科学系）

Joy Jia Yin Lim, Ye He, Jifan Yu, Xin Cong, Daniel Zhang-Li, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu

备注：: Accepted at The Web Conference 2026 (WWW'26)

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AI总结：

Pxplore通过整合强化学习与LLM，实现基于目标驱动的个性化学习路径规划，提升学习路径的连贯性和有效性。

摘要：

个性化学习路径规划（PLPP）旨在设计能够与个体目标相匹配的自适应学习路径。尽管大型语言模型（LLMs）在个性化学习体验方面展现出潜力，但现有方法往往缺乏目标导向的规划机制。我们引入Pxplore，一种新的PLPP框架，整合了基于强化学习的训练范式和由LLM驱动的教育架构。我们设计了一种结构化的学习者状态模型和一个自动化的奖励函数，将抽象目标转化为可计算的信号。我们训练结合了监督微调（SFT）和群体相对策略优化（GRPO）的策略，并将其部署在真实世界的学习平台上。广泛的实验验证了Pxplore在生成连贯、个性化和目标导向的学习路径方面的有效性。我们在此发布代码和数据集，网址为https://github.com/Pxplore/pxplore-algo。

英文摘要：

Personalized Learning Path Planning (PLPP) aims to design adaptive learning paths that align with individual goals. While large language models (LLMs) show potential in personalizing learning experiences, existing approaches often lack mechanisms for goal-aligned planning. We introduce Pxplore, a novel framework for PLPP that integrates a reinforcement-based training paradigm and an LLM-driven educational architecture. We design a structured learner state model and an automated reward function that transforms abstract objectives into computable signals. We train the policy combining supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO), and deploy it within a real-world learning platform. Extensive experiments validate Pxplore's effectiveness in producing coherent, personalized, and goal-driven learning paths. We release our code and dataset at https://github.com/Pxplore/pxplore-algo.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。