The Agency Gap: How Generative Ai Literacy Shapes Independent Writing After Ai Support

Generative AI (GenAI) tools are rapidly transforming higher education, yet little is known about how students' GenAI literacy shapes their ability to perform independently once such support is removed. This study investigates what we term the agency gap, introduced as the extent to which GenAI literacy predicts student writing performance in contexts that require self-initiation and regulation. Seventy-nine medical and nursing students completed multimodal academic writing tasks based on visual data, supported either by a reactive or proactive GenAI chatbot, followed by a parallel task without AI support. Writing was evaluated across insightfulness, visual data integration, organisation, linguistic quality, and critical thinking. Results showed that GenAI literacy predicted independent writing performance only in the reactive condition, where students had to actively mobilise their own strategies. Mediation analyses revealed no indirect effect via in-task performance, indicating that GenAI literacy acts as a direct, task-general competence rather than a proxy for domain knowledge or other literacies. By contrast, proactive scaffolding equalised outcomes across literacy levels, reducing reliance on learners' GenAI literacy. The agency gap highlights when GenAI literacy matters most, with implications for designing equitable AI-supported learning environments that both leverage and mitigate differences in students' GenAI literacy.