A Random Walk Modeling Framework for Boosting the Creativity of Humans and AI

Innovation depends on generating ideas that are not merely more numerous but meaningfully different. This article offers a practical framework for improving creativity in both people and generative artificial intelligence (AI). It models ideation as movement through a network of concepts, where common associations are easy to reach and more creative ideas lie on less-traveled paths. The framework explains why familiar techniques work and gives managers a systematic way to create new ones: narrow attention through constraints, shift the context, or delay output before selecting an answer. Across experiments with human participants and large language models, simple interventions—such as asking for alliterative responses, introducing an unusual environment, or requesting the fifth idea rather than the first—raised the novelty of responses compared with baseline prompts. For practice, the findings show that creativity can be improved through small changes to prompts, workflows, brainstorming rules, and AI-copilot interfaces. For policy, the study suggests that AI adoption should focus not only on model capability, but also on how human-AI interaction is structured to preserve diversity, reduce fixation, and support innovation across organizations.