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[2602.17999] Aurora: Neuro-Symbolic AI Driven Advising Agent
Summary
Aurora is a neuro-symbolic AI advising agent designed to enhance academic advising in higher education by providing timely, policy-compliant recommendations through a combination of symbolic reasoning and natural language processing.
Why It Matters
The increasing advisor-to-student ratios in higher education create significant barriers to effective academic advising. Aurora addresses these challenges by leveraging advanced AI techniques to deliver scalable, accurate, and explainable recommendations, potentially transforming student support systems and improving graduation rates.
Key Takeaways
- Aurora combines retrieval-augmented generation and symbolic reasoning for effective advising.
- It significantly improves the accuracy of recommendations compared to traditional LLMs.
- The system operates with low latency, making it suitable for real-time advising scenarios.
- Aurora's modular design allows for easy integration with existing academic systems.
- The approach enhances equity in student support by providing consistent guidance.
Computer Science > Human-Computer Interaction arXiv:2602.17999 (cs) [Submitted on 20 Feb 2026] Title:Aurora: Neuro-Symbolic AI Driven Advising Agent Authors:Lorena Amanda Quincoso Lugones, Christopher Kverne, Nityam Sharadkumar Bhimani, Ana Carolina Oliveira, Agoritsa Polyzou, Christine Lisetti, Janki Bhimani View a PDF of the paper titled Aurora: Neuro-Symbolic AI Driven Advising Agent, by Lorena Amanda Quincoso Lugones and 6 other authors View PDF HTML (experimental) Abstract: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. ...