Instrumental and integrative motivations predict intention to use generative AI chatbots for English learning among undergraduates with non English majors in Chinese private universities

This study integrates Gardner’s Socio-Educational Model of Second Language Motivation (SEMSLM) with the Technology Acceptance Model (TAM) to examine the influence pathways of instrumental motivation (INS) and integrative motivation (INT) on Chinese non-English major undergraduates’ intentions to use generative AI (Gen-AI) chatbots. A mixed random and convenience sampling approach was employed to survey students from four private universities in China, yielding 729 valid responses. The study measured students’ instrumental and integrative motivation, as well as TAM constructs including perceived usefulness (PU), perceived ease of use (PE), and behavioral intention to use (IU). Structural equation modeling (SEM) was applied to analyze both direct and indirect effects of motivation on IU, investigating the mediating mechanisms of TAM variables. Participants demonstrated a clear preference for domestically developed Gen-AI chatbots and reported relatively high instrumental motivation and perceived ease of use. Instrumental motivation significantly influenced IU through three mediation paths, with a stronger effect via PE than PU. In contrast, integrative motivation influenced IU solely through PU. Overall, the total effect of instrumental motivation was substantially higher than that of integrative motivation. The study highlights differential pathways through which distinct motivational types shape Gen-AI chatbot adoption, providing insights for supporting low-motivation learner groups and advancing understanding of AI-assisted language learning.