Perceived AI interactivity and motivated learning behaviors in AI-Supported EFL learning in higher education: the mediating roles of flow and self-efficacy

Abstract

This study examined the relationship between perceived AI interactivity (PAII) and motivated learning behaviors (MLBs) among English as a Foreign Language learners in China, with particular attention to the mediating roles of flow experience and self-efficacy. Data from 290 valid responses collected through online questionnaires were analyzed using descriptive statistics, correlation analysis, and partial least squares structural equation modeling (PLS-SEM). The results showed that PAII was not directly associated with MLBs, but exerted significant indirect effects through self-efficacy and through the sequential pathway involving flow experience and self-efficacy. By contrast, the indirect effect through flow experience alone was not significant. Specifically, higher levels of PAII were associated with stronger flow experience, which in turn was associated with higher self-efficacy and subsequently with stronger MLBs. These findings highlight the importance of flow experience and self-efficacy in explaining how perceived AI interactivity is associated with MLBs. The study also offers practical implications for the design of AI-supported learning environments and for teaching practices aimed at fostering learners' flow experience and self-efficacy.