A Recommender System Architecture for University Curriculum Advising

Abstract

Effective academic advising plays a crucial role in student success, yet universities face challenges in optimizing advising processes and course enrollment. This task is complicated by the fact that several graduation requirements have to be met while also taking the students' interests into account. Academic advising has historically been performed by a skilled human adviser. Universities can optimize course planning and help students make informed decisions about their academic path with recommender systems. This case study develops a goal-based agent recommender system based on a large language model tailored to undergraduate students, depending on curriculum requirements, prerequisite dependencies, and student preferences. The developed recommendation system helps universities increase student advising efficiency and create more intuitive and student-centric curricula. We show how to structure and process complex curriculum data to create an algorithm-ready environment, simplifying the relationships between degree requirements and course offerings. This study evaluates multiple algorithms based on recommendation accuracy, computational efficiency, and their ability to meet degree requirements while fostering academic engagement. By streamlining course selection and exploring possible degree paths, the system may also help students graduate on time and navigate complex curricula. This system also collects important metrics to accurately predict student enrollment for classes, enabling college departments to plan their course offerings better. The system poses a significant benefit to university advising offices by reducing advisor workloads and encouraging student engagement, advancing the academic achievement of the entire student body.