Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review

TL;DR

This paper combines systematic review and Monte Carlo simulation to quantify how student perceptions of generative AI influence educational success, highlighting usability factors as key contributors.

Contribution

It introduces a novel hybrid methodology integrating literature synthesis with probabilistic modeling to assess perceptions and outcomes related to GenAI in higher education.

Findings

Usability factors like System Efficiency significantly impact success scores.

Perceptions of Learning Burden negatively influence educational outcomes.

The framework enables estimation of uncertainty in perception-based success measures.

Abstract

The rapid development of generative artificial intelligence (GenAI) tools such as ChatGPT has intensified interest in their role in higher education, particularly in how students perceive and use them and how these perceptions may relate to educational outcomes. This study employs a hybrid methodological approach that combines a PRISMA-guided systematic literature review with simulation-based modeling to examine student perceptions of GenAI in higher education. Nineteen empirical articles published between 2023 and 2025 were identified through a Scopus-based review, and thematic synthesis was used to organize the emerging patterns in the literature. Of these, six studies reported item-level means and standard deviations suitable for probabilistic modeling. From this subset, one well-structured Likert-scale dataset was selected as a canonical example for inverse-variance-weighted Monte Carlo simulation. The simulation generated a composite perception-based Success Score, enabling estimation of both central tendency and uncertainty under different thematic configurations. The findings indicate that usability-related factors, particularly System Efficiency and Learning Burden, exert the greatest influence on the composite score under the specified weighting scheme, while other themes also contribute positively but more modestly. The study offers a transparent and privacy-preserving bridge between thematic synthesis and predictive probabilistic modeling, providing a reproducible framework for linking GenAI perceptions to educational outcomes in future research.