IntelliCode：一种基于集中式学习者建模的多智能体大语言模型辅导系统 (IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling)

LLM-based tutors are typically single-turn assistants that lack persistent representations of learner knowledge, making it difficult to provide principled, transparent, and long-term pedagogical support. We introduce IntelliCode, a multi-agent LLM tutoring system built around a centralized, versioned learner state that integrates mastery estimates, misconceptions, review schedules, and engagement signals. A StateGraph Orchestrator coordinates six specialized agents: skill assessment, learner profiling, graduated hinting, curriculum selection, spaced repetition, and engagement monitoring, each operating as a pure transformation over the shared state under a single-writer policy. This architecture enables auditable mastery updates, proficiency-aware hints, dependency-aware curriculum adaptation, and safety-aligned prompting. The demo showcases an end-to-end tutoring workflow: a learner attempts a DSA problem, receives a conceptual hint when stuck, submits a corrected solution, and immediately sees mastery updates and a personalized review interval. We report validation results with simulated learners, showing stable state updates, improved task success with graduated hints, and diverse curriculum coverage. IntelliCode demonstrates how persistent learner modeling, orchestrated multi-agent reasoning, and principled instructional design can be combined to produce transparent and reliable LLM-driven tutoring.

翻译：基于大语言模型（LLM）的辅导系统通常是单轮对话式助手，缺乏对学习者知识的持久化表征，因此难以提供有原则、透明且长期的教学支持。本文介绍 IntelliCode，这是一个围绕集中式、版本化学习者状态构建的多智能体 LLM 辅导系统，该状态集成了掌握程度估计、错误概念、复习计划与参与度信号。一个状态图编排器协调六个专用智能体：技能评估、学习者画像、渐进式提示、课程选择、间隔重复和参与度监控，每个智能体在单一写入策略下作为对共享状态的纯转换进行操作。该架构支持可审计的掌握程度更新、基于熟练度的提示、依赖感知的课程调整以及安全对齐的提示生成。演示展示了一个端到端的辅导工作流：学习者尝试解决一个数据结构与算法（DSA）问题，在遇到困难时收到概念性提示，提交修正后的解决方案，并立即看到掌握程度更新和个性化的复习间隔。我们报告了使用模拟学习者的验证结果，显示了稳定的状态更新、渐进式提示带来的任务成功率提升以及多样化的课程覆盖。IntelliCode 展示了如何将持久化学习者建模、编排化的多智能体推理以及有原则的教学设计相结合，以产生透明且可靠的 LLM 驱动式辅导。