Jennifer Spanagel

Papers

Total Citations

H-Index

About

Jennifer Spanagel is a researcher at the intersection of artificial intelligence and education, with a primary focus on enhancing STEM learning through advanced natural language processing. Her most-cited work, "Evaluating the Impact of Advanced LLM Techniques on AI Lecture Tutors for a Robotics Course" (2025), has already garnered 3 citations, signaling early recognition of its practical significance. In this study, Spanagel investigates how large language models can be leveraged to create more responsive, adaptive lecture tutors, specifically within the challenging domain of robotics education. Her contributions lie in bridging the gap between cutting-edge LLM capabilities and real-world pedagogical needs, offering a framework for deploying AI tutors that can dynamically adjust explanations and problem-solving support. This work not only demonstrates the potential of AI to personalize technical instruction but also provides a replicable model for integrating LLMs into course design. Spanagel’s research is particularly notable for its hands-on, application-driven approach, directly addressing the scalability and engagement challenges faced in robotics curricula. As her citation count grows, her findings are poised to influence both AI developers and educators seeking to harness generative models for interactive, student-centered learning environments.