一种整合神经认知诊断、有界能力自适应测试与大语言模型驱动反馈的闭环个性化学习智能体
A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback
AI总结：
本文提出EduLoop-Agent，整合神经认知诊断、有界能力自适应测试和大语言模型反馈，构建“诊断-推荐-反馈”闭环，实现个性化学习，实验验证其有效性与可部署性。
摘要：
随着信息技术的进步，教育正从一刀切的指令式教学转向个性化学习。然而，大多数方法将建模、题目选择和反馈割裂处理，而非形成一个闭环。这导致了粗糙或不透明的学生模型、受假设约束且忽略诊断后验的自适应性，以及通用且不可操作的反馈。为解决这些局限性，本文提出了一种端到端的个性化学习智能体EduLoop-Agent，它整合了神经认知诊断模型（NCD）、有界能力估计计算机化自适应测试策略（BECAT）和大语言模型（LLMs）。NCD模块在知识点层面提供对学生掌握程度的细粒度估计；BECAT动态选择后续题目以最大化相关性和学习效率；LLMs将诊断信号转化为结构化、可操作的反馈。这些组件共同构成了一个“诊断—推荐—反馈”的闭环框架。在ASSISTments数据集上的实验表明，NCD模块在响应预测上取得了强劲性能，同时提供了可解释的掌握程度评估。自适应推荐策略提高了题目相关性和个性化程度，基于LLM的反馈则针对识别出的薄弱环节提供了有针对性的学习指导。总体而言，结果表明所提出的设计有效且可实际部署，为智能教育中生成个性化学习轨迹提供了一条可行路径。
英文摘要：
As information technology advances, education is moving from one-size-fits-all instruction toward personalized learning. However, most methods handle modeling, item selection, and feedback in isolation rather than as a closed loop. This leads to coarse or opaque student models, assumption-bound adaptivity that ignores diagnostic posteriors, and generic, non-actionable feedback. To address these limitations, this paper presents an end-to-end personalized learning agent, EduLoop-Agent, which integrates a Neural Cognitive Diagnosis model (NCD), a Bounded-Ability Estimation Computerized Adaptive Testing strategy (BECAT), and large language models (LLMs). The NCD module provides fine-grained estimates of students' mastery at the knowledge-point level; BECAT dynamically selects subsequent items to maximize relevance and learning efficiency; and LLMs convert diagnostic signals into structured, actionable feedback. Together, these components form a closed-loop framework of ``Diagnosis--Recommendation--Feedback.'' Experiments on the ASSISTments dataset show that the NCD module achieves strong performance on response prediction while yielding interpretable mastery assessments. The adaptive recommendation strategy improves item relevance and personalization, and the LLM-based feedback offers targeted study guidance aligned with identified weaknesses. Overall, the results indicate that the proposed design is effective and practically deployable, providing a feasible pathway to generating individualized learning trajectories in intelligent education.
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。
