Factors influencing the intention to use generative artificial intelligence in educational systems: a meta-analysis

Abstract

Background: The rapid development of Generative Artificial Intelligence (Gen AI) has significantly impacted education. However, empirical findings on the intention to use Gen AI vary due to differing theoretical approaches. This study aims to clarify key influencing variables through a comprehensive meta-analysis. Methods: A total of 53 empirical studies were analyzed, including 17 influencing factors and 4 types of moderating variables. We extracted correlation coefficients from the included studies to assess their association with Gen AI use intention. Results: Strong correlations were observed between use intention and perceived usefulness (r = 0.566) as well as attitude (r = 0.564). Moderate correlations are observed for performance expectancy (r = 0.499), perceived ease of use (r = 0.471), hedonic motivation (r = 0.456), personal innovation (r = 0.373), trust (r = 0.368), effort expectancy (r = 0.352), social influence (r = 0.329), and perceived intelligence (r = 0.326). Weak correlations are found for emotion (r = 0.255) and technology anxiety (r = − 0.239). Moderating effects were found: gender ratio influenced nine variables, country category affected effort expectancy and innovation, age moderated several cognitive and emotional variables, and measurement tools impacted multiple constructs including social influence and perceived ease of use. Conclusion: This study identifies core psychological and contextual factors influencing Gen AI use intention in educational settings. It offers theoretical clarity and practical guidance for optimizing Gen AI integration in education, supporting more effective adoption across diverse learner groups. Trial registration: This study has been registered with PROSPERO, number: 639170.