Abstract
We investigate how generative AI shapes creative performance and human-AI interaction in an open-ended writing task that employs a laboratory experiment in which participants are randomly assigned to either receive access to a large language model (ChatGPT-4.2) or not. Creative performance is measured by the average score assigned by independent evaluators recruited through the Prolific platform, and detailed logs of human-AI interaction are analyzed to measure AI use, prompting intensity, ideation requests, and the textual overlap between AI outputs and participants' final writings. Three main results emerge. First, AI access increases performance, but the gain is entirely driven by active use: participants with access who do not submit queries perform no better than those without AI. Second, the relationship between interaction intensity and performance is concave, peaking at roughly eight queries, consistent with iterative exploration rather than mechanical copying. Third, structural mediation analyses show that ideation requests affect performance primarily indirectly, by increasing downstream incorporation of AI-generated language; the direct effect of requesting an idea from the AI is negligible once execution-stage reliance is accounted for. We further document heterogeneity in AI reliance: cultural capital (proxied by books owned) predicts lower AI use, while prior AI exposure predicts higher use. By contrast, incentive schemes have limited effects on both outcomes and AI-related behaviors.