Satisficing vs. Maximizing in Prompt Writing: Trait and Task Effects in Human-AI Interaction

Generative AI systems are increasingly used for cognitively demanding tasks, yet little is known about how psychological factors shape user prompting behavior. This study investigates the role of individual satisficing tendencies in maximizing behavior when selecting prompt strategies across different task domains. In an online vignette experiment with 132 participants, individuals selected between satisficing and maximizing prompt options in five problem-solving scenarios. Satisficing tendencies were assessed using the Short Maximization Inventory, with algorithm aversion and prompt-writing competence included as controls. Linear mixed models showed that stronger satisficing tendencies were associated with reduced maximizing behavior, while higher self-reported competence predicted more maximizing. Participants maximized more in job-related and creative tasks, but satisficed more in writing and technical support tasks, suggesting that task characteristics shape prompting strategies. The results demonstrate that individual differences systematically affect interactions with generative AI. This highlights the importance of considering psychological dispositions in future research on human–AI collaboration.