RAG ‘n’ Roll Industrial Management Game: Building and Testing a Large Language Model Assistant for Operations Education

ABSTRACT Simulation‐based games are widely used in engineering and management studies to support decision‐making in complex and dynamic environments. This study presents an exploratory case study of the design, implementation, and evaluation of a Retrieval‐Augmented Generation (RAG) assistant that uses 10 years of instructor‐answered student questions to provide context‐aware support during gameplay. The RAG chatbot was designed and implemented to support facilitation by clarifying game mechanics, terminology, and previously addressed strategic considerations, helping teams navigate difficult decision‐making processes without providing direct solutions. The assistant was implemented on a low‐code, web‐based artificial intelligence (AI) orchestration platform with an integrated vector database and tested during development. Perceived usefulness was evaluated through post‐course student feedback across two independent markets with identical game mechanics. About 61% of students reported using the bot, while 39% did not. Analysis of survey data indicates students generally rated the chatbot as moderately useful, with extreme evaluations rare; at the same time, the overall semantic orientation of feedback was predominantly positive. Findings suggest that the chatbot functioned successfully as a contextual support mechanism rather than a direct driver of performance outcomes. This study demonstrates the feasibility of designing and evaluating an AI‐enabled learning support tool in an industrial engineering and management course.