Graduation Term

Fall 2024

Degree Name

Master of Science (MS)

Department

Department of Technology

Committee Chair

Dr. Borinara Park

Committee Member

Dr. Jaby Mohammed

Committee Member

Dr. Sundeep Inti

Abstract

This study addresses challenges in Discrete Event Simulation (DES) education, where traditional methods focus heavily on theory but lack hands-on, real-world application. To bridge this gap, the research explores using AI, specifically GPT models, to create interactive, accessible learning experiences in DES. By comparing structured and unstructured questioning approaches, the study demonstrates that structured interactions using a Work Breakdown Structure (WBS) produce more accurate, realistic DES models. Findings show that structured AI-driven methods help learners better understand and apply DES concepts, providing a practical pathway from theory to application. This research highlights AI’s potential to transform DES education, supporting diverse learning needs and advancing model quality and engagement. Future directions include expanding AI-driven DES training across various fields to enhance simulation-based learning and operational decision-making.

Access Type

Thesis-Open Access

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