Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China
Wenqian Lin, Peijie Jiang
Abstract
Generative artificial intelligence (GAI) has attracted attention in education as a tool to help college students learn mathematics. This study analyzed the factors influencing their use of GAI by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) and focusing on mathematics motivation. This study involved 331 Chinese college students and used partial least squares structural equation modeling (PLS-SEM) for data analysis. The results showed that college students' behavioral intention to use GAI to support their mathematics learning was directly influenced by performance expectancy, social influence, personal innovativeness, and mathematics motivation. Mathematics motivation, facilitating conditions, individual demand, and behavioral intention, had direct effects on college students' use of GAI in mathematics. The most significant factor influencing both intention and behavior was mathematics motivation. Effort expectancy and individual demand did not affect the intention to use GAI in mathematics learning. In addition, there were important positive moderating effects, including individual demand, of mathematics motivation in the structural model on usage behavior and behavioral intention regarding usage behavior. The results of this study could help to identify the key influences on college students' use of new technologies in mathematics learning and provide informative insights for the application of AI technologies in mathematics learning in the future.