Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking

Abstract

Large language models are transforming the creative process by offering unprecedented capabilities to algorithmically generate ideas. While these tools can enhance human creativity when people co-create with them, it's unclear how this will impact unassisted human creativity. We conducted two large pre-registered parallel experiments involving 1,100 participants attempting tasks targeting the two core components of creativity, divergent and convergent thinking. We compare the effects of two forms of large language model (LLM) assistance -- a standard LLM providing direct answers and a coach-like LLM offering guidance -- with a control group receiving no AI assistance, and focus particularly on how all groups perform in a final, unassisted stage. Our findings reveal that while LLM assistance can provide short-term boosts in creativity during assisted tasks, it may inadvertently hinder independent creative performance when users work without assistance, raising concerns about the long-term impact on human creativity and cognition.

CHI 2025

References in corpus (7)

• Art and the science of generative AI: A deeper dive
• Generative artificial intelligence enhances creativity but reduces the diversity of novel content
• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
• Homogenization Effects of Large Language Models on Human Creative Ideation
• Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models
• Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation
• Machine Culture

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