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

TL;DR

This study investigates how large language models influence human creativity, revealing that while they boost short-term performance during collaboration, they may impair independent creative abilities in the long run.

Contribution

It provides large-scale experimental evidence on the effects of LLM assistance on both divergent and convergent thinking, highlighting potential long-term drawbacks.

Findings

LLM assistance improves immediate creative task performance.

Assistance may reduce independent creativity in unassisted tasks.

Long-term impacts on human creativity are potentially negative.

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.