Foundation models are increasingly used to personalize learning, yet many systems still assume fixed curricula or coarse progress signals, limiting alignment with learners' day-to-day needs. At the other extreme, lightweight incidental systems offer flexible, in-the-moment content but rarely guide learners toward mastery. Prior work privileges either continuity (maintaining a plan across sessions) or initiative (reacting to the moment), not both, leaving learners to navigate the trade-off between recency and trajectory-immediate relevance versus cumulative, goal-aligned progress. We present LOOM, an agentic pipeline that infers evolving learner needs from recent LLM conversations and a dynamic learner memory graph, then assembles coherent learning materials personalized to the learner's current needs, priorities, and understanding. These materials link adjacent concepts and surface gaps as tightly scoped modules that cumulatively advance broader goals, providing guidance and sustained progress while remaining responsive to new interests. We describe LOOM's end-to-end architecture and working prototype, including conversation summarization, topic planning, course generation, and graph-based progress tracking. In a formative study with ten participants, users reported that LOOM's generated lessons felt relevant to their recent activities and helped them recognize knowledge gaps, though they also highlighted needs for greater consistency and control. We conclude with design implications for more robust, mixed-initiative learning pipelines that integrate structured learner modelling with everyday LLM interactions.

翻译：基础模型正日益用于个性化学习，然而许多系统仍假设固定的课程体系或粗略的进度信号，限制了与学习者日常需求的契合。另一方面，轻量级即时学习系统虽提供灵活、即时的内容，却很少引导学习者实现知识掌握。先前的研究往往侧重连续性（跨会话维持学习计划）或主动性（响应即时需求）中的单一维度，未能兼顾两者，导致学习者不得不在时效性与长期轨迹——即即时关联性与累积性、目标导向的进展——之间进行权衡。本文提出LOOM，一种智能学习管道，通过分析近期大语言模型对话记录与动态学习者记忆图谱，推断学习者不断变化的需求，进而组装符合其当前需求、优先级及理解水平的连贯学习材料。这些材料以紧密聚焦的模块形式呈现，关联相邻概念并揭示知识缺口，在累积推进更广泛学习目标的同时，既提供学习指导与持续进展，又能灵活响应新的兴趣点。我们阐述了LOOM的端到端架构与工作原型，包括对话摘要、主题规划、课程生成及基于图谱的进度追踪。在一项包含十名参与者的形成性研究中，用户反馈LOOM生成的课程与其近期活动相关，并有助于识别知识缺口，但也指出需要更强的系统一致性与用户控制权。最后，我们讨论了设计启示，旨在构建更稳健、混合主动性的学习管道，将结构化学习者建模与日常大语言模型交互相融合。