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This study introduces a Monte Carlo simulation framework to quantify student success metrics in higher education by modeling perceptions of GenAI usability and efficiency. By leveraging inverse-variance weighting on aggregated literature data, the research demonstrates that system efficiency and learning burden are the primary drivers of composite success scores. The approach offers a reproducible, privacy-preserving method for synthesizing disparate empirical findings into probabilistic models for educational technology assessment.
Quantifying Perception-Based Student Success with Generative AI: An Exploratory Monte Carlo Simulation
arXiv:2507.01062v4 Announce Type: replace-cross Abstract: 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. This study develops an exploratory Monte Carlo simulation framework for quantifying perception-based student success in the context of GenAI use. 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, with System Efficiency and Learning Burden receiving the largest inverse-variance weight and therefore the strongest influence on the composite score. The study offers a transparent, reproducible, and privacy-preserving proof-of-concept framework linking structured literature search, item-level summary statistics, and probabilistic modelling.
Abstract: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. This study develops an exploratory Monte Carlo simulation framework for quantifying perception-based student success in the context of GenAI use. 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, with System Efficiency and Learning Burden receiving the largest inverse-variance weight and therefore the strongest influence on the composite score. The study offers a transparent, reproducible, and privacy-preserving proof-of-concept framework linking structured literature search, item-level summary statistics, and probabilistic modelling.
Published in Education Sciences. This article is an extended and substantially revised version of a conference paper presented at the Melbourne Institute of Technology ICETE Conference, Sydney, NSW, Australia, 9-10 February 2026. The earlier conference version is available at DOI https://doi.org/10.25397/ppny-f488
Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
K.3.1; H.5.2; I.2.7
arXiv:2507.01062 [cs.CY]
(or arXiv:2507.01062v4 [cs.CY] for this version)
https://doi.org/10.3390/educsci16060832 DOI(s) linking to related resources
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