A Custom GPT for Executive MBA Students: A Case Study in Enhancing Learning
Richard P. Waterman, Brandon D. Lafving, Ceren Okar, Nupur Jain · Stat · 2025
ABSTRACT This paper presents the development and implementation of a custom GPT‐based tool designed to enhance Executive MBA students' learning experience in Wharton's core business statistics course. The AI‐driven assistant was tailored to the specific course content, offering an interactive platform to review complex statistical concepts and improve comprehension. Insights from a multidisciplinary team are presented, focusing on the system's architecture, including the Retrieval‐Augmented Generation (RAG) framework, feedback mechanisms, prompt design and integration of course materials to guide replication and scaling across academic settings. The study explores the tool's alignment with student expectations, emphasizing strategies to build trust and engagement with AI‐generated outputs and the value of linking course materials and lecture recordings to key concepts. The importance of a feedback loop for continuous improvement and trust enhancement is highlighted. Additionally, the paper addresses the tool's limitations, demonstrates its impact through usage statistics and outlines planned enhancements to refine functionality. Critical considerations such as scalability, ethics and data privacy are also discussed. This case study provides valuable lessons for educators and technologists aiming to leverage large language models in higher education, offering a roadmap for integrating AI tools effectively into academic programs.