A Locally Deployed RAG-Based Academic Advising System for Course Selection
For students and educational institutions, this system offers a privacy-preserving tool to improve course sequencing, but the work is incremental as it applies existing RAG techniques to a new domain.
The paper proposes a locally deployed RAG-based academic advising system that uses syllabus data to help students with course selection and prerequisite understanding, addressing information overload and limited advising resources. No concrete performance numbers are provided.
The correct sequence of courses in the curriculum based on prerequisites between courses is of great importance for students to develop their knowledge and skills holistically. However, students crafting this sequence in isolation frequently struggle with recognition limitations and information overload that leads to confusion. Simultaneously, education institutions encounter difficulties in providing adequate academic advice for the correct sequence due to limited education resources. To address these challenges, we propose a locally deployed RAG-based academic advising system grounded in syllabus information. By combining large language models with retrieval from structured syllabus data, the system is designed to support course selection, prerequisite understanding, and personalized study planning in a privacy-preserving manner.