Reasonable Sense of Direction: Making Course Recommendations Understandable with LLMs

Course recommendation systems play an essential role in academic institutions for students to find courses that align with their interests and graduation requirements. However, due to their “black-box” nature, recommendation systems often lack transparency and interpretability, leading to challenges in trust and usability. Our proposed framework leverages Large Language Models (LLMs) to generate clear, human-readable explanations based on course content by drawing connections between the existing courses taken by the student and recommended courses.