Personalized learning systems have emerged as a promising approach to enhance student outcomes by tailoring educational content, pacing, and feedback to individual needs. However, most existing systems remain fragmented, specializing in either knowledge tracing, diagnostic modeling, or resource recommendation, but rarely integrating these components into a cohesive adaptive cycle. In this paper, we propose ALIGNAgent (Adaptive Learner Intelligence for Gap Identification and Next-step guidance), a multi-agent educational framework designed to deliver personalized learning through integrated knowledge estimation, skill-gap identification, and targeted resource recommendation.ALIGNAgent begins by processing student quiz performance, gradebook data, and learner preferences to generate topic-level proficiency estimates using a Skill Gap Agent that employs concept-level diagnostic reasoning to identify specific misconceptions and knowledge deficiencies. After identifying skill gaps, the Recommender Agent retrieves preference-aware learning materials aligned with diagnosed deficiencies, implementing a continuous feedback loop where interventions occur before advancing to subsequent topics. Extensive empirical evaluation on authentic datasets from two undergraduate computer science courses demonstrates ALIGNAgent's effectiveness, with GPT-4o-based agents achieving precision of 0.87-0.90 and F1 scores of 0.84-0.87 in knowledge proficiency estimation validated against actual exam performance.

翻译：个性化学习系统通过为个体需求定制教育内容、进度与反馈，已成为提升学生学业成果的有效途径。然而，现有系统大多功能割裂，仅专注于知识追踪、诊断建模或资源推荐中的单一环节，鲜少将这些组件整合为连贯的自适应循环。本文提出ALIGNAgent（面向差距识别与步骤引导的自适应学习者智能），这是一个通过集成知识评估、技能差距识别与定向资源推荐来实现个性化学习的多智能体教育框架。ALIGNAgent首先处理学生的测验表现、成绩册数据与学习偏好，利用技能差距智能体生成主题级能力评估；该智能体采用概念级诊断推理机制，以识别具体错误概念与知识缺陷。在识别技能差距后，推荐智能体将检索符合诊断缺陷且兼顾偏好的学习材料，并建立持续反馈循环——在进入后续主题前实施干预措施。基于两门本科计算机科学课程真实数据集的实证评估表明，ALIGNAgent具有显著效能：基于GPT-4o的智能体在知识能力评估中达到0.87-0.90的精确率与0.84-0.87的F1分数，该结果已通过实际考试表现验证。