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

要旨

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.

受賞
Honorable Mention
著者
Harsh Kumar
University of Toronto, Toronto, Ontario, Canada
Jonathan Vincentius
University of Toronto, Toronto, Ontario, Canada
Ewan Jordan
University of Toronto, Toronto, Ontario, Canada
Ashton Anderson
University of Toronto, Toronto, Ontario, Canada
DOI

10.1145/3706598.3714198

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714198

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: AI-Assisted Creativity

Annex Hall F203
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
日本語まとめ
読み込み中…