Examining longitudinal development of writing motivation in the GenAI context: A self-determination theory perspective

Abstract

This longitudinal study examines the development of motivation among a sample of 261 Chinese EFL student writers in generative AI (GenAI)-supported writing contexts through the lens of Self-Determination Theory (SDT). Using three-wave data collection, the focus was on how GenAI influences four key motivational constructs: autonomy, competence, relatedness, and identified regulation, and the developmental trajectory of motivation over time. Results from mixed-effects modeling reveal significant time and group interactions, demonstrating that GenAI use leads to substantial motivational gains over time. The results from Gradient Boosting Machine for Time Series (GBMT) indicated that the EFL learners’ motivation development follows curvilinear trajectories, characterized by rapid initial growth that gradually plateaus. Theoretically, the findings extend SDT's application to AI-mediated learning by demonstrating how GenAI scaffolds psychological needs: fostering competence through adaptive feedback, autonomy via personalized support, and relatedness and identified regulation through simulated social interaction. The study contributes to emerging discourse on technology-enhanced motivation while highlighting the need for proficiency-sensitive implementation strategies in EFL writing contexts.