Date
Publisher
arXiv
The exponential development of generative artificial intelligence (GenAI)
technologies like ChatGPT has raised increasing curiosity about their use in
higher education, specifically with respect to how students view them, make use
of them, and the implications for learning outcomes. This paper employs a
hybrid methodological approach involving a systematic literature review and
simulation-based modeling to explore student perceptions of GenAI use in the
context of higher education. A total of nineteen empirical articles from 2023
through 2025 were selected from the PRISMA-based search targeting the Scopus
database. Synthesis of emerging patterns from the literature was achieved by
thematic categorization. Six of these had enough quantitative information,
i.e., item-level means and standard deviations, to permit probabilistic
modeling. One dataset, from the resulting subset, was itself selected as a
representative case with which to illustrate inverse-variance weighting by
Monte Carlo simulation, by virtue of its well-designed Likert scale format and
thematic alignment with the use of computing systems by the researcher.
The simulation provided a composite "Success Score" forecasting the strength
of the relationship between student perceptions and learning achievements.
Findings reveal that attitude factors concerned with usability and real-world
usefulness are significantly better predictors of positive learning achievement
than affective or trust-based factors. Such an interdisciplinary perspective
provides a unique means of linking thematic results with predictive modelling,
resonating with longstanding controversies about the proper use of GenAI tools
within the university.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
