Exploring mechanisms of effective informal GenAI-supported second language speaking practice: a cognitive-motivational model of achievement emotions

Conversational generative artificial intelligence (GenAI) has emerged as a promising tool for second language (L2) speaking practice, but the mechanisms behind its effectiveness remain underexplored. This study aims to explore these mechanisms through the lens of the cognitive-motivational model of achievement emotions in the control-value theory. Specifically, we investigate how emotions (enjoyment, boredom, and curiosity), cognitive processing, and the ideal L2 self interact to influence speaking performance. The sample consisted of 158 Chinese L2 majors engaging in GenAI-assisted speaking practice in informal contexts. Using Partial Least Square-Structural Equation Modeling (PLS-SEM) with Smart PLS 4 software, key findings include that gender did not affect the constructs under study, but GenAI competence positively influenced speaking performance. Enjoyment had a direct effect on cognitive processing, which in turn enhanced speaking performance, though it did not influence the ideal L2 self. Curiosity positively influenced the ideal L2 self and speaking performance, but had no effect on cognitive processing. Boredom, however, did not affect either cognitive processing or the ideal L2 self. The study contributes theoretically by advancing understanding of how psychological factors shape L2 speaking performance in GenAI contexts. Pedagogically, it offers insights into optimizing GenAI for language practice.