A Locally Deployed RAG-Based Academic Advising System for Course Selection
Researchers have developed a new academic advising system that uses a Retrieval-Augmented Generation (RAG) approach to help students select courses. This system, deployed locally, leverages large language models and syllabus data to assist with course sequencing, prerequisite understanding, and personalized study plans. The goal is to address challenges students face with information overload and limited institutional advising resources, while maintaining privacy. AI
IMPACT Provides a privacy-preserving tool for students to navigate course selection and academic planning.