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

Generative artificial intelligence (GenAI) tools such as ChatGPT have attracted growing attention in higher education, particularly in relation to how students perceive their usefulness, usability, and educational value. However, existing studies are often descriptive and rarely translate perception data into exploratory quantitative indicators that can support structured evaluation under uncertainty. To address this gap, this study develops an exploratory Monte Carlo simulation framework for quantifying perception-based student success in the context of GenAI use. The term Perception-Based Student Success Score is used here as an exploratory proxy indicator derived from students’ positive evaluations of usability, efficiency, learnability, and perceived integration; it does not represent direct academic achievement, grades, retention, or objectively measured learning outcomes. A PRISMA-informed structured literature search in Scopus identified nineteen empirical studies published between 2023 and 2025, of which six reported item-level means and standard deviations suitable for probabilistic modelling. One coherent 10-item, 5-point Likert-scale usability-oriented instrument was selected as a canonical proof-of-concept dataset and used to parameterise an inverse-variance-weighted Monte Carlo simulation generating 10,000 synthetic observations. The results show that the weighting structure substantially influences the simulated outcome. In particular, System Efficiency and Learning Burden received the largest inverse-variance weight and therefore had the strongest influence on the composite score. This dominance should be interpreted cautiously because low variance in Likert-scale data may reflect response homogeneity or ceiling effects rather than substantive importance alone. The study offers a transparent, reproducible, and privacy-preserving proof-of-concept framework linking structured literature search, item-level summary statistics, and probabilistic modelling.

First publicly released on arXiv on 30 June 2025. Later published in Education Sciences (2026, Volume 16, Article 832). This article is an extended and substantially revised version of a conference paper presented at the Melbourne Institute of Technology ICETE Conference, Sydney, Australia, 9-10 February 2026. The earlier conference version is available under DOI 10.25397/ppny-f488.