Longitudinal Analysis of L 2 Writing Enjoyment Trajectory in AI ‐Mediated Writing: Examining the Roles of AI Feedback Literacy and Learner– AI Interactivity

With the increasing integration of artificial intelligence (AI) into second language (L2) writing, learners' emotional experiences in AI‐mediated writing contexts have attracted growing scholarly attention. However, limited research has examined how L2 writing enjoyment develops over time or how it is influenced by learner‐ and interaction‐related factors in AI‐mediated environments. Guided by Complex Dynamic Systems Theory (CDST), this longitudinal study examines the developmental trajectory of L2 writing enjoyment and investigates the predictive roles of AI feedback literacy and perceived learner–AI interactivity. Data were collected from 328 participants at three time points across one semester and analysed using latent growth curve modelling (LGCM) to capture the developmental trajectory of writing enjoyment. Our results revealed a modest but significant upward increase in students' L2 writing enjoyment across the semester. In addition, both AI feedback literacy and perceived learner–AI interactivity significantly predicted the initial level of writing enjoyment as well as the rate of its development over time. These findings provide empirical support for a CDST‐informed perspective on the dynamic development of emotions in L2 writing. Our findings also identify key pathways through which writing enjoyment can be leveraged in AI‐mediated writing contexts, highlighting the roles of learners' AI competencies and their perceived learner–AI interactivity.