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.

翻译：生成式人工智能（GenAI）工具（如ChatGPT）的快速发展，加剧了人们对其在高等教育中作用的关注，尤其是学生如何感知和使用这些工具，以及这些感知可能与教育成果之间的关系。本研究采用了一种混合方法论，将PRISMA指导的系统性文献综述与基于模拟的建模相结合，以考察高等教育中学生对GenAI的感知。通过基于Scopus的文献检索，识别出2023年至2025年间发表的19篇实证文章，并采用主题综合法对文献中涌现的模式进行组织。其中，6项研究报告了适用于概率建模的项目水平均值和标准差。从该子集中，选取了一个结构良好的Likert量表数据集作为典型示例，用于进行逆方差加权蒙特卡洛模拟。该模拟生成了一个基于感知的综合成功得分，从而能够在不同主题配置下估计集中趋势和不确定性。研究结果表明，在特定权重方案下，与可用性相关的因素（尤其是系统效率和学习负担）对综合得分影响最大，而其他主题也产生正向但较为适中的贡献。本研究为主题综合与预测概率建模之间提供了一座透明且保护隐私的桥梁，为未来研究中将GenAI感知与教育成果相关联提供了一个可复现的框架。