Exploring the dynamics of enjoyment in traditional and ChatGPT-assisted L2 Writing: A complex dynamic systems theory-based multiple case study

The recent emergence of generative artificial intelligence has been transforming traditional writing practices, leading to new patterns of emotional experience among learners in the writing process. Drawing on Complex Dynamic Systems Theory, this study investigated the dynamic trajectories of enjoyment experienced by four Chinese undergraduate EFL learners across two types of writing tasks: one completed in a traditional context and the other with ChatGPT's assistance. Adopting an idiodynamic approach, this study traced the moment-to-moment fluctuations of enjoyment in the two writing contexts and further explored the factors contributing to the dynamic changes in learners' enjoyment. Data were collected from multiple sources, including students' writing products, video recordings, plotted enjoyment curves, and semi-structured interviews. The findings revealed both inter-individual differences and intra-individual variability in learners' enjoyment patterns. Although learners generally experienced greater enjoyment in the ChatGPT-assisted context, ChatGPT's facilitative role in L2 writing remained conditional and learner-dependent. Thematic analysis revealed that learners' enjoyment trajectories emerged from the dynamic interaction among individual factors, including language proficiency, writing motivation, AI communication competence, self-regulation during writing, as well as contextual factors, including topic familiarity, teacher support, and the broader educational environment. These results highlight the non-linear and context-sensitive nature of enjoyment and have implications for L2 writing pedagogy.