The Predictive Effects of Perceptions of AI‐generated Feedback on Feedback‐Seeking Behavior: A Prospective Study Testing the Cost‐value Framework

ABSTRACT The concept of feedback‐seeking behavior provides a novel perspective for understanding learners’ proactive roles in feedback processes. Although AI‐generated feedback has been promoted as a supplement to teacher feedback, limited evidence exists regarding language learners’ AI feedback‐seeking behavior or its proximal antecedents, particularly in skills beyond writing. Drawing on a cost‐value framework and employing a prospective design, this study investigates the predictive effects of language learners’ perceptions of AI‐generated feedback on their use of feedback‐seeking strategies in subsequent translation tasks. Questionnaire data collected from 344 undergraduates enrolled in a translation course were analyzed using structural equation modeling. The results showed that the perceived values of AI feedback significantly predicted the frequency of feedback‐seeking strategies, whereas perceived costs did not. The effect of the utility value of AI feedback on feedback‐seeking strategies was greater than that of the intrinsic value of AI feedback. These distinct motivational patterns of AI feedback‐seeking behavior suggest that the cost‐value framework should be further refined to better suit AI‐mediated contexts and enhance its explanatory power. This study has important implications for research on learners’ AI feedback‐seeking behavior and for integrating AI feedback into language instruction.