How Technology Affordances Foster Continuance Intention in English Speaking Practice with AI‐Based Conversational Agents: A Stimulus‐Organism‐Response Perspective

This study examined factors influencing Chinese college EFL learners’ intention to continue using AI‐based conversational agents for oral English practice. An integrated model combining technology affordance theory and the expectation‐confirmation model was established based on the S‐O‐R framework. Using 522 valid questionnaire responses, this study applied PLS‐SEM for data analysis. The results revealed that the three identified technology affordances served as external stimuli shaping learners' internal states, which in turn fostered their continuance intention. This work enriches relevant theories and offers practical insights. Theoretically, this study advances current knowledge by integrating technology affordances with post‐adoption psychology within a unified framework, thereby extending the theoretical boundaries of existing models. Practically, the findings provide guidance for AI developers to optimize user experience and for educators to integrate these tools into language learning environments.