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 stan
The proliferation of generative AI tools in higher education necessitates quantifying their impact, especially concerning student perception and success.
Understanding how students perceive and utilize GenAI is crucial for educators and institutions to adapt curricula and policies effectively, influencing future workforce readiness.
The ability to quantify perception-based student success provides a framework for evaluating the educational efficacy and integration of generative AI rather than anecdotal observation.
- · Educational researchers
- · Higher education institutions
- · AI tool developers
- · Traditional assessment methods
- · Institutions resistant to AI integration
Universities will increasingly adopt frameworks to measure the impact of GenAI on student learning outcomes and perceptions.
This data will inform curriculum development, leading to new pedagogical approaches that integrate GenAI tools more effectively.
The perceived usefulness and usability of GenAI by students could influence broader societal adoption and expectations for AI in professional settings.
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Read at arXiv cs.AI