# Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists arXiv: https://arxiv.org/abs/2501.12374 ยท HTML: https://mlsanity.com/paper/2501.12374/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 118 references that are in the map. - First posted on arXiv 21 Jan 2025. ## Builds on (most cited first, 30 of 36) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023) - [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023) - [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025) - [Red-Teaming the Stable Diffusion Safety Filter](https://mlsanity.com/paper/2210.04610/) (2022) - [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023) - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021) - [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023) - [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023) - [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023) - [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023) - [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022) - [Automating Customer Service using LangChain: Building custom open-source GPT Chatbot for organizations](https://mlsanity.com/paper/2310.05421/) (2023) - [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023) - [Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering](https://mlsanity.com/paper/2303.13534/) (2023) - [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024) - [A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians](https://mlsanity.com/paper/2405.20956/) (2024) - [Experimental Narratives: A Comparison of Human Crowdsourced Storytelling and AI Storytelling](https://mlsanity.com/paper/2310.12902/) (2023) - [Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity](https://mlsanity.com/paper/2303.12003/) (2023) - [ChatGPT as Research Scientist: Probing GPT's Capabilities as a Research Librarian, Research Ethicist, Data Generator and Data Predictor](https://mlsanity.com/paper/2406.14765/) (2024) - [Can AI Be as Creative as Humans?](https://mlsanity.com/paper/2401.01623/) (2024) - [Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence](https://mlsanity.com/paper/2309.14379/) (2023) - [Conceptual similarity and communicative need shape colexification: an experimental study](https://mlsanity.com/paper/2103.11024/) (2021) - [Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usage](https://mlsanity.com/paper/2305.09537/) (2023) - [AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks](https://mlsanity.com/paper/2401.06178/) (2024) - [One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humor](https://mlsanity.com/paper/2501.11433/) (2025) - [Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art](https://mlsanity.com/paper/2401.15497/) (2024) - [Exploring the Impact of AI-generated Image Tools on Professional and Non-professional Users in the Art and Design Fields](https://mlsanity.com/paper/2406.10640/) (2024) - [Compression ensembles quantify aesthetic complexity and the evolution of visual art](https://mlsanity.com/paper/2205.10271/) (2022) ## Built on by (most cited first, 4 of 4) - [GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design](https://mlsanity.com/paper/2508.15227/) (2025) - [Machine-Assisted Grading of Nationwide School-Leaving Essay Exams with LLMs and Statistical NLP](https://mlsanity.com/paper/2601.16314/) (2026) - [Collective creativity in hybrid societies](https://mlsanity.com/paper/2609.02620/) (2026) - [Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn](https://mlsanity.com/paper/2610.12792/) (2026) ## Abstract (arXiv) Generative AI's novel capacities raise questions about the future role of human expertise: does AI level the playing field between professional artists and laypeople, or does expertise enhance AI use? Do the cognitive skills experts make use of in analyzing and drawing visual art also transfer to using these new tools? This pre-registered study conducts experimental comparisons between 50 professional artists and a demographically matched sample of laypeople. Our interdisciplinary team developed two tasks involving image replication and creative image creation, assessing their copying accuracy and divergent thinking. We implemented a bespoke platform for the experiment, powered by a modern text-to-image AI. Results reveal artists produced more accurate copies and more divergent ideas than lay participants, highlighting a skill transfer of professional expertise - even to the confined space of generative AI. We also explored how well an exemplary vision-capable large language model (GPT-4o) would fare: on par in copying and slightly better on average than artists in the creative task, although never above best humans. These findings highlight the importance of integrating artistic skills with AI, suggesting a potential for collaborative synergy that could reshape creative industries and arts education.