The agency gap in AI-supported writing: How reactive and proactive agent designs shape multimodal reasoning

Generative AI is becoming part of academic writing, but its educational value depends on how control is shared between learner and system. This study examined an agency gap: performance differences that may arise when AI agent initiative is misaligned with learners’ generative AI literacy. Seventy-nine medical and nursing students completed two multimodal analytical writing tasks using healthcare simulation data visualisations. They were randomly assigned to a reactive agent that responded only when prompted or a proactive agent that provided sequenced questions and feedback. Generative AI literacy was measured using the validated 20-item Generative AI Literacy Assessment Test. Epistemic network analysis showed that proactive interaction created stronger links among conceptual reasoning, evidence use, and constructive engagement, whereas reactive interaction was more factual and procedural. Ordinal regression showed that generative AI literacy predicted immediate independent writing performance after support was removed, particularly for visual data integration, critical thinking, and overall quality. Condition-specific mediation estimates showed a literacy–performance association in the reactive condition but not in the proactive condition; however, the indirect effects and literacy-by-design interactions were not significant. This pattern is consistent with smaller literacy-related performance differences under proactive scaffolding, but it does not establish a compensatory causal effect. Learner reflections indicated that effective AI writing support requires contextual feedback, dialogic scaffolding, and calibration of initiative to learner needs and task complexity. These findings position interaction design as a potential mechanism for supporting equitable and agency-supportive educational AI agents.