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.

翻译：生成式人工智能（GenAI）工具（如ChatGPT）在高等教育中日益受到关注，尤其是在学生对其有用性、易用性和教育价值的认知方面。本研究开发了一个探索性蒙特卡洛模拟框架，用于量化GenAI使用情境中基于感知的学生成功指标。通过基于PRISMA原则的结构化文献检索，在Scopus数据库中筛选出2023年至2025年间发表的19项实证研究，其中6项报告了适用于概率建模的条目层面均值与标准差。选取一套一致的10条目、5点李克特量表的易用性导向工具作为典型概念验证数据集，并基于该数据集参数化逆方差加权蒙特卡洛模拟，生成10,000个合成观测值。结果表明，加权结构对模拟结果产生显著影响，其中系统效率与学习负担条目获得最大逆方差权重，从而对综合评分产生最强影响。本研究提供了一个透明、可重复且保护隐私的概念验证框架，将结构化文献检索、条目层面汇总统计与概率建模有机结合。