Academic advising in higher education is under severe strain, with advisor-to-student ratios commonly exceeding 300:1. These structural bottlenecks limit timely access to guidance, increase the risk of delayed graduation, and contribute to inequities in student support. We introduce Aurora, a modular neuro-symbolic advising agent that unifies retrieval-augmented generation (RAG), symbolic reasoning, and normalized curricular databases to deliver policy-compliant, verifiable recommendations at scale. Aurora integrates three components: (i) a Boyce-Codd Normal Form (BCNF) catalog schema for consistent program rules, (ii) a Prolog engine for prerequisite and credit enforcement, and (iii) an instruction-tuned large language model for natural-language explanations of its recommendations. To assess performance, we design a structured evaluation suite spanning common and edge-case advising scenarios, including short-term scheduling, long-term roadmapping, skill-aligned pathways, and out-of-scope requests. Across this diverse set, Aurora improves semantic alignment with expert-crafted answers from 0.68 (Raw LLM baseline) to 0.93 (+36%), achieves perfect precision and recall in nearly half of in-scope cases, and consistently produces correct fallbacks for unanswerable prompts. On commodity hardware, Aurora delivers sub-second mean latency (0.71s across 20 queries), approximately 83X faster than a Raw LLM baseline (59.2s). By combining symbolic rigor with neural fluency, Aurora advances a paradigm for accurate, explainable, and scalable AI-driven advising.

翻译：高等教育中的学业指导正面临严峻压力，指导老师与学生比例通常超过300:1。这种结构性瓶颈限制了学生及时获得指导的机会，增加了延迟毕业的风险，并加剧了学生支持的不平等现象。本文介绍Aurora——一个模块化神经符号指导智能体，它统一了检索增强生成（RAG）、符号推理和规范化课程数据库，以提供符合政策、可验证的大规模建议。Aurora整合了三个核心组件：（i）采用Boyce-Codd范式（BCNF）的课程目录架构以确保培养方案规则的一致性；（ii）基于Prolog的逻辑引擎用于先修课程与学分要求的强制校验；（iii）经过指令微调的大语言模型，为其建议提供自然语言解释。为评估性能，我们设计了覆盖常规与边缘案例的结构化测试集，包括短期选课规划、长期学业路径设计、技能导向的培养方案以及超范围请求。在该多样化测试集上，Aurora将专家标准答案的语义对齐度从0.68（原始大语言模型基线）提升至0.93（+36%），在近半数适用场景中实现完美的精确率与召回率，并对无法回答的提示始终生成正确的降级处理。在商用硬件上，Aurora的平均延迟低于1秒（20次查询平均0.71秒），较原始大语言模型基线（59.2秒）提速约83倍。通过融合符号系统的严谨性与神经网络的流畅性，Aurora为推动精准、可解释、可扩展的AI驱动学业指导范式提供了新路径。