From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries

Most universities in the United States encourage their students to explore academic areas before declaring a major and to acquire academic breadth by satisfying a variety of requirements. Each term, students must choose among many thousands of offerings, spanning dozens of subject areas, a handful of courses to take. The curricular environment is also dynamic, and poor communication and search functions on campus can limit a student's ability to discover new courses of interest. To support both students and their advisers in such a setting, we explore a novel Large Language Model (LLM) course recommendation system that applies a Retrieval Augmented Generation (RAG) method to the corpus of course descriptions. The system first generates an 'ideal' course description based on the user's query. This description is converted into a search vector using embeddings, which is then used to find actual courses with similar content by comparing embedding similarities. We describe the method and assess the quality and fairness of some example prompts. Steps to deploy a pilot system on campus are discussed.

From Interets to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries

Most universities in the United States encourage their students to explore academic areas before declaring a major and to acquire academic breadth by satisfying a variety of requirements. Each term, students must choose among many thousands of offerings, spanning dozens of subject areas, a handful of courses to take. The curricular environment is also dynamic, and poor communication and search functions on campus can limit a student's ability to discover new courses of interest. To support both students and their advisers in such a setting, we explore a novel Large Language Model (LLM) course recommendation system that applies a Retrieval Augmented Generation (RAG) method to the corpus of course descriptions. The system first generates an 'ideal' course description based on the user's query. This description is converted into a search vector using embeddings, which is then used to find actual courses with similar content by comparing embedding similarities. We describe the method and assess the quality and fairness of some example prompts. Steps to deploy a pilot system on campus are discussed.