AFFECTIVE DYNAMICS IN AI-ASSISTED AND TRADITIONAL SECOND LANGUAGE WRITING: AN IDIODYNAMIC STUDY INFORMED BY COMPLEXITY DYNAMIC SYSTEMS THEORY
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Second language (L2) writing often involves rapid, idiosyncratic shifts in anxiety, motivation, and perceived control, yet these dynamics remain underexplored in generative AI-mediated writing. Guided by Complex Dynamic Systems Theory (CDST), this study examined second-by-second affective trajectories as two Turkish EFL learners with contrasting profiles (one typically anxious, one confident) completed comparable timed academic essays with and without a GPT-based writing assistant. Methodologically, this qualitative two-case study employed a CDST-informed idiodynamic approach, combining second-by-second self-ratings of anxiety, motivation, and perceived control during video-replay sessions with stimulated recall interviews. The anxious writer showed pronounced volatility in the traditional condition, with anxiety spikes and concurrent drops in motivation and control; AI support attenuated these fluctuations by restoring perceived control and stabilizing affect. The confident writer maintained consistently high control and motivation and low anxiety in both conditions, using AI mainly for reassurance. Overall, perceived control emerged as a key attractor shaping affective self-regulation, and AI operated as a context-sensitive co-regulator rather than a uniform aid. Pedagogically and technologically, the findings suggest that AI-enhanced writing environments should provide adaptive, profile-sensitive scaffolding, while teachers should monitor learners’ momentary sense of control and integrate metacognitive support to foster sustainable self-regulation.
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