Academic advising encompasses a broad range of student support services, including guidance on transfer credits, graduation requirements, major and minor declarations, and institutional policies. However, traditional advising methods often face challenges related to scalability, consistency, and accessibility. This paper presents a Retrieval-Augmented Generation (RAG)-based chatbot designed to optimize general academic advising by leveraging generative AI. The model aims to enhance data retrieval, improving the precision and relevance of advising responses. We evaluate the chatbot’s effectiveness based on response accuracy, user satisfaction, and its ability to address nuanced advising scenarios. Our findings indicate that the RAG-based approach improves efficiency and accessibility in academic advising, providing a scalable AI-driven decision support system for higher education institutions.
