Quantifying Perception-Based Student Success with Generative AI: An Exploratory Monte Carlo Simulation

The study introduces a Monte Carlo simulation framework to quantify perception-based student success in the context of generative AI tools like ChatGPT. Utilizing a 10-item, 5-point Likert-scale usability instrument derived from a structured literature review, the simulation generated 10,000 synthetic observations, revealing that the weighting structure significantly affects the outcomes, particularly highlighting System Efficiency and Learning Burden. This framework provides a reproducible method for assessing the educational impact of GenAI, which can aid practitioners in evaluating and optimizing AI tools in educational settings.