Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis
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Abstract (arXiv)
Generative artificial intelligence (GenAI) is increasingly used to support a wide range of human tasks, yet empirical evidence on its effect on creativity remains scattered. Can GenAI generate ideas that are creative? To what extent can it support humans in generating ideas that are both creative and diverse? In this study, we conduct a meta-analysis to evaluate the effect of GenAI on the performance in creative tasks. For this, we first perform a systematic literature search, based on which we identify n = 28 relevant studies (m = 8214 participants) for inclusion in our meta-analysis. We then compute standardized effect sizes based on Hedges' g. We compare different outcomes: (i) how creative GenAI is; (ii) how creative humans augmented by GenAI are; and (iii) the diversity of ideas by humans augmented by GenAI. Our results show no significant difference in creative performance between GenAI and humans (g = -0.05), while humans collaborating with GenAI significantly outperform those working without assistance (g = 0.27). However, GenAI has a significant negative effect on the diversity of ideas for such collaborations between humans and GenAI (g = -0.86). We further analyze heterogeneity across different GenAI models (e.g., GPT-3.5, GPT-4), different tasks (e.g., creative writing, ideation, divergent thinking), and different participant populations (e.g., laypeople, business, academia). Overall, our results position GenAI as an augmentative tool that can support, rather than replace, human creativity-particularly in tasks benefiting from ideation support.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 70 references that are in the map. First posted on arXiv 22 May 2025.
Builds on
16 of 70 references.
References · 16
- 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researcherscited by 363
- 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
- 2024Benchmark Data Contamination of Large Language Models: A Surveycited by 147
- 2023A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writingcited by 99
- 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 83
- 2025We're Different, We're the Same: Creative Homogeneity Across LLMscited by 42
- 2024The Effects of Generative AI on Design Fixation and Divergent Thinkingcited by 37
- 2022A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Makingcited by 29
- 2023Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativitycited by 24
- 2024How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experimentcited by 22
- 2024Can AI Be as Creative as Humans?cited by 17
- 2025One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humorcited by 12
4 more
- 2024Large Language Models show both individual and collective creativity comparable to humanscited by 11
- 2024On the stochastics of human and artificial creativitycited by 2
- 2024Artificial intelligence and the internal processes of creativitycited by 2
- 2025AI Delivers Creative Output but Struggles with Thinking Processescited by 2
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2026AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompterscited by 8
- 2026Probabilistic Calibration Is a Trainable Capability in Language Modelscited by 2
- 2026EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agentscited by 1
- 2025The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativitycited by 0
- 2025Future of AI Models: A Computational perspective on Model collapsecited by 0
- 2026AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters21 May 2026
- 2026Probabilistic Calibration Is a Trainable Capability in Language Models12 May 2026
- 2026EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents26 Mar 2026
- 2025The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativity3 Nov 2025
- 2025Future of AI Models: A Computational perspective on Model collapse29 Oct 2025