The Impact of Generative Artificial Intelligence Use on Perceived English Learning Achievement: The Roles of Use Behavior and Task–Technology Fit

The rapid advancement of generative artificial intelligence (GAI) has intensified interest in its potential to support English learning in higher education. However, the mechanisms through which students’ perceptions and motivations translate into learning achievement remain unclear. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task–Technology Fit (TTF) theory, this study investigates how undergraduate students’ use of GAI relates to perceived English learning achievement and under what conditions these associations are amplified. Using covariance-based structural equation modeling (CB-SEM), data from 537 undergraduate students across five public universities in China were analyzed. The findings indicate that performance expectancy, effort expectancy, facilitating conditions, perceived competitiveness, and artificial intelligence self-efficacy significantly predict GAI use. In turn, use behavior mediates their relationships with perceived English learning achievement. Task–Technology Fit further moderates the link between use behavior and learning achievement, with stronger associations observed when GAI functionalities are perceived as closely aligned with task requirements. These results highlight the importance of use behavior and task alignment in explaining how GAI is associated with students’ perceived English learning achievement and extend technology acceptance research within AI-supported language learning contexts.