Dynamic reciprocal associations of AI-assisted L2 writing task emotions in data-driven learning: a dynamic structural equation modeling
Mirosław Pawlak, Mariusz Kruk, Majid Elahi Shirvan, Tahereh Taherian, Sedigheh Karimpour
Abstract
This study explores the longitudinal reciprocal associations between task enjoyment, task anxiety, and task boredom, and their interactions in relation to artificial intelligence (AI)-assisted L2 writing achievement within a data-driven learning context. Drawing on the experience sampling method, data were collected from 67 participants across four sessions of an L2 academic writing workshop, where they rated their emotions in real-time during task-based activities every 10 min, leading to a total of 1,688 observations. Dynamic structural equation modeling (DSEM) revealed that task enjoyment increased, while boredom and anxiety decreased over time. At the within-individual level, task anxiety showed the highest stability, followed by task enjoyment, with task boredom being the least persistent. Findings also indicated that task emotions exerted significant reciprocal influences on each other, with stronger impacts from task enjoyment. At the between-individual level, learners with higher levels of task enjoyment and boredom did not necessarily carry these emotions over into future sessions, whereas learners with higher task anxiety tended to maintain this emotional state over time. Additionally, higher levels of task enjoyment and lower levels of task anxiety and boredom predicted better L2 writing outcomes, with the persistence of these emotions playing a crucial role. This research provides insights into the emotional dynamics of task-based learning, offering practical implications for designing data-driven learning environments that foster positive emotions.