ORCID

0000-0003-4599-0936

Keywords

Artificial Intelligence, Business Simulations, Coaching, Performance Variability, Management Education

Subject Categories

Business | Curriculum and Instruction | Educational Technology

Abstract

Large classes change what instruction looks like. In hybrid business courses with high enrollment, it becomes harder to respond to individual students, and performance can suffer as a result. This study looked at what happens when that gap is addressed in different ways during an eight-week business simulation at the University of Central Florida. Six course sections were divided into three study groups. One group worked without support, one received instructor guidance, and one used AI tools that included a course search system and generative responses. Student outcomes were based on weekly profit and loss recorded in Sim Companies. The analysis followed five questions. Did human instructor facilitation improve student performance outcomes? Did AI-based facilitation improve student performance outcomes? How did performance compare across all three conditions? How much did results fluctuate week to week? How did performance change over time? Kolb’s framework informed the interpretation, but the focus stayed on observed patterns in the data. Both supported groups finished with stronger results than the unsupported group, with differences of about three thousand dollars per week. There was no clear separation between the instructor and AI groups on average performance. The differences became apparent through other analyses. Students using AI tended to hold steadier results across the eight weeks and did not drop off at the end. Students working with an instructor showed more uneven patterns, with gains and setbacks that suggest ongoing adjustment in their decisions. When asked to reflect on their experience, those students were more likely to explain what they did in terms of business reasoning. Taken together, the results point in two directions. AI support appears capable of producing comparable outcomes at scale. At the same time, the path students take to get there is not the same. That difference matters for how learning is interpreted in simulation-based settings.

Completion Date

2026

Semester

Summer

Committee Chair

Li, Yao

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Modeling and Simulation

Format

PDF

Document Type

Dissertation

Language

English

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