Dialogue patterns in human-AI co-creation: how interaction structure shapes output and process

Generative AI (GenAI) systems are transforming cognitive work, yet we lack a clear picture of how different human–AI interaction structures affect the output participants produce, the process they go through, and how they perceive the AI partner. We conducted a between-subjects experiment (N = 244) comparing four implemented interaction designs for a user persona creation task: human-lead expand, AI-lead propose, iterative critique, and free dialogue. Iterative critique produced higher task-appropriate creativity than AI-lead propose, driven by appropriateness and the composite creativity score; free dialogue and human-lead expand produced more descriptively rich language than iterative critique; originality did not differ across conditions. A between-persona similarity analysis found that interaction structure converges iterative critique participants on a common persona, while AI-lead propose diversifies scenarios relative to chance. Participants in the condition producing the highest task-appropriate creativity perceived the AI as a tool and felt personal ownership, while those in more conversational conditions perceived the AI as a collaborative partner. We conclude that interaction pattern choice should be driven by what outcome and experience the designer is aiming for, and we offer preliminary recommendations for matching interaction patterns to those goals, including the possibility of pattern switching across phases of a single workflow.