Perceived AI interactivity and Chinese EFL learners’ motivated learning behaviors: the moderating role of daily AI usage duration

The integration of Artificial intelligence (AI) tools in language education has reshaped learners' perceptions and behavioral responses. While earlier studies have examined the cognitive and affective effects of learners' perceived AI interactivity (PAII), less is known about its influence on motivated behavioral engagement. Motivated learning behaviors (MLBs) are crucial for sustained language learning. Accordingly, this study investigated how four dimensions of PAII predict MLBs among 171 Chinese English as a foreign language (EFL) learners, and whether daily AI usage duration (DAIUD) moderates these effects. Participants completed a validated questionnaire measuring PAII, MLBs, and DAIUD. Statistical results indicated that all four PAII dimensions significantly and positively predicted MLBs, with LC exerting the strongest predictive effect and R the weakest. Moreover, DAIUD significantly moderated the paths for R, LC, and P, strengthening their positive associations with MLBs, but it did not significantly moderate the LE path. This study expands the literature by applying a multidimensional construct of PAII to explain learners' MLBs in Chinese EFL contexts. Moreover, these findings offer practical implications for designing learner-centered AI tools, and for educators providing tailored guidance for EFL learners in AI-supported learning.