A Custom GPT for Executive MBA Students: A Case Study in Enhancing Learning

Music City Center 

This paper presents a case study on the development and implementation of a custom GPT-based tool designed to enhance the learning experience for Executive MBA students at Wharton, in reviewing and studying a core Business Statistics course. The project aimed to provide students with an interactive, AI-driven resource tailored to their specific learning needs, offering an interactive way to review complex statistical concepts and improve comprehension.
We share insights from the multidisciplinary team, highlighting the integration of diverse expertise in designing and deploying the system. Key components of the project architecture, including the Retrieval-Augmented Generation (RAG) framework, feedback mechanisms, system prompt design, and integration of course materials, are discussed to provide a comprehensive guide for those interested in replicating or scaling similar AI tools across different academic settings.
The paper explores how the tool aligns with student expectations, including strategies for fostering trust and engagement with AI-generated outputs, the importance of linking relevant course materials and lecture recordings to specific concepts for deeper learning, emphasizing the importance of the feedback loop for continuous improvement and trust enhancement.
In addition, the paper discusses the limitations of the current platform, outlines usage statistics to demonstrate its impact on student engagement and highlights future enhancements to further refine the tool. This case study also addresses critical considerations such as scaling across other courses, ethical and data privacy protocols, and strategies for balancing AI-driven insights with human-led instruction. This paper offers valuable lessons for educators and technologists seeking to leverage large language models in higher education.

Custom GPT-based tool

Core Statistics Class

Retrieval-Augmented Generation (RAG) framework

Feedback mechanisms

Trust development

Student engagement metrics