
This study proposes a goal-based agent recommender system for undergraduate students using a large language model, tailored to curriculum requirements, prerequisites, and student preferences. The system optimizes course selection, streamlines degree path exploration, and predicts enrollment metrics, reducing advisor workloads and promoting academic engagement, ultimately enhancing student success and increasing the overall academic achievement of the student body.
May 28, 2025
30-minute research presentation on applying GraphRAG (Neo4j) and autonomous tool-calling agents to solve degree requirement planning for university students.
May 5, 2025
Poster presentation on recommender system architectures, course constraint satisfaction, and student degree planning algorithms at NCUR 2025.
Apr 9, 2025
30-minute conference presentation introducing goal-based agent recommender systems for undergraduate curriculum advising at the AAAI Spring Symposium.
Mar 24, 2025