How performance expectancy and autonomy satisfaction influence translanguaging interactions with AI chatbots

This study investigates the role of performance expectancy and autonomy satisfaction in predicting key learner outcomes – intercultural sensitivity, intrinsic motivation, metacognition, self-efficacy, and task strategies – within the context of translanguaging interactions with AI chatbots. Building on theories of AI affordance and self-determination, the study examines both linear and nonlinear relationships between these predictors and learner outcomes, while exploring whether there is convergence or divergence between performance expectancy and autonomy satisfaction. We recruited 203 undergraduate students enrolled in an elective second language (L2) course on intercultural communication. Participants completed three case studies addressing workplace-related intercultural dilemmas, engaging in translanguaging practices during AI chatbot interactions. Multilevel polynomial regression models and response surface analyses revealed that both performance expectancy and autonomy satisfaction independently predicted positive learner outcomes. Moreover, autonomy satisfaction exhibited a quadratic relationship with several outcomes, suggesting accelerating benefits at higher levels. Convergence between the predictors was associated with optimal outcomes, while divergence negatively impacted psychological and cognitive dimensions, particularly intercultural sensitivity and intrinsic motivation. These findings highlight the intricate interplay between technological affordances and psychological needs in AI-mediated multilingual learning.