Student Support

Student support research focuses on developing personalized learning experiences to improve student outcomes and address scalability challenges in education. Current efforts leverage large language models (LLMs) and reinforcement learning algorithms, often integrated with ontology-driven frameworks or traditional information retrieval systems, to create adaptive learning paths, intelligent tutoring systems, and automated urgency detection for student inquiries. This work aims to enhance learning efficiency, engagement, and timely intervention, ultimately impacting student retention and success while optimizing resource allocation for educators.