Idiographic self-regulated affordance uptake in AI-mediated language learning

Research on AI-mediated language learning has often emphasized aggregate measures of performance and engagement, yet little is known about how learners individually perceive and enact affordances in sustained interaction with conversational agents. This study addressed this gap through an idiographic multiple-case analysis of four Vietnamese adults who engaged in eight sessions of self-directed English practice with ChatGPT. Interaction logs and stimulated recall interviews were analyzed using a five-dimensional affordance framework consisting of perceptibility, valence, intentionality, compositionality, and normativity to trace how uptake unfolded across time. The findings show that each learner developed a distinct trajectory. Some orchestrated affordances proactively and in compositional ways, others constrained uptake through cautious or normative orientations and shifts in affect, often triggering turning points in how action potentials were noticed and realized. These results demonstrate that affordances are not static features of AI systems but emergent relations that acquire significance only in action and ecologically. The study advances affordance theory in language education by showing that variability is not incidental but constitutive of learning ecologies, and it suggests that effective AI-mediated pedagogy requires preparing learners to notice, revalue, and creatively combine affordances in ways that support autonomy and sustained engagement.