A Meta-analysis of College Students' Intention to Use Generative Artificial Intelligence

TL;DR

This meta-analysis examines factors influencing college students' intention to use generative AI, highlighting key variables like performance expectancy and attitudes, and providing insights for educational technology implementation.

Contribution

It synthesizes 27 empirical studies to identify main predictors of students' GenAI usage intention, offering a comprehensive understanding of influencing factors.

Findings

Performance expectancy has a strong positive correlation (r=0.389) with usage intention.

Attitudes are highly correlated (r=0.576) with behavioral intention.

Gender moderates attitudes but not other variables.

Abstract

It is of critical importance to analyse the factors influencing college students' intention to use generative artificial intelligence (GenAI) to understand and predict learners' learning behaviours and academic outcomes. Nevertheless, a lack of congruity has been shown in extant research results. This study, therefore, conducted a meta-analysis of 27 empirical studies under an integrated theoretical framework, including 87 effect sizes of independent research and 33,833 sample data. The results revealed that the main variables are strongly correlated with students' behavioural intention to use GenAI. Among them, performance expectancy (r = 0.389) and attitudes (r = 0.576) play particularly critical roles, and effort expectancy and habit are moderated by locational factors. Gender, notably, only moderated attitudes on students' behavioural intention to use GenAI. This study provides valuable insights for addressing the debate regarding students' intention to use GenAI in existed research, improving educational technology, as well as offering support for school decision-makers and educators to apply GenAI in school settings.