cs.AINov 6, 2026
SaveWhat Is the Value of AI-Involved Art? Evidence From a Juried In-Person Exhibition
Organizations: Georgetown University, USA · Max Planck Institute for Software Systems, Germany
Abstract
What is the value of AI-involved art, and to whom does it belong? We begin to answer these questions using data from an in-person, juried AI art exhibition. Our study involved artists, lawyers, and computer scientists (N=209) as either creators or evaluators of AI-involved artworks. We found that nearly half of evaluators reported willingness to buy an artwork, with pricing correlating with perceived artistic value---creativity and visual appeal---as well as the effort necessary to create it. In contrast, three-quarters of creators reported willingness to sell at prices correlating only with effort rather than artistic value. Both groups viewed prompting users and AI developers as deserving rewards and rights, leaving training data artists behind. Disciplinary training shaped these views: participants with legal training favored distributed reward models. These findings have implications for future research on human-AI creative collaboration and policy debates regarding AI-involved works.
Figures & tables
Figure 1. Experimental methodology flow diagram showing two parallel evaluation streams from left to right: (A) Creators ( ) generate artworks using AI-only or mixed-media approaches. (B) Creators produce diverse outputs within the AI-only or mixed-media categories. (C) Creators complete self-evaluations. (D) Evaluators undergo protocol training and assess artworks either with knowledge of the creation process (Experts) or without such knowledge (Peer Evaluators). All images shown in the diagram were selected for exhibition. (E) The 12 selected artworks proceed to the Public and Online Exhibition. Flow diagram of recruitment, screening, random assignment to conditions, and rating procedure.
Figure 2. Study interfaces. Left: creator application used to generate artworks via text prompts; the interface automatically logged time, prompt iterations, and prompt text. Right: evaluator survey showing the image-only judgment screen (no origin labels or process information), where participants rated creativity, effort, technical quality, and visual appeal, and then allocated rights and value. Study interfaces
Figure 3. Mixed media category workflow. Creators could choose to begin with traditional handcrafted artwork (left), upload it to the AI art generation platform (center), and iteratively prompt the system to produce their final composition (right). Creators could also choose to begin with the AI art generation platform before continuing with traditional artistic tools. Mixed media workflow
Figure 4. The twelve highest-rated artworks selected for the in-person public exhibition. Titles were chosen by the creators. top rate artworks
Figure 5. Mean ratings of perceived effort and artistic value dimensions by creators and evaluators. (a) Evaluators rated creativity (mean = 3.59), technical quality (mean = 4.34), visual appeal (mean = 3.86), and effort (mean = 3.88). (b) Creators rated their own work on creativity (mean = 3.86), technical quality (mean = 2.90), and effort (mean = 3.74). All ratings are on a 1—5 scale. Error bars represent 95% confidence intervals Mean ratings of effort and artistic value
Figure 6. Valuation patterns and pricing preferences for AI-involved artwork by participant group. (a) Distribution of prices creators would sell for, and evaluators would pay, (b) Percentage of participants who would allocate monetary compensation to each stakeholder group. Error bars represent standard errors capped at 5%. Valuation patterns and pricing preferences
Figure 7. Monetary prize allocation preferences across stakeholder groups comparing creators and evaluators. Each pair of bars shows the percentage of creators (blue) and evaluators (orange) who selected each stakeholder group as a prize recipient. Solid colors indicate exclusive selection (only that recipient chosen), while striped patterns indicate shared selection (that recipient chosen along with others) Monetary prize allocation
| Artwork | Artistic Statement |
|---|---|
| “It reminded me of a screen that used to be in the Smithsonian Museum of Asian Art, which is no longer on display, and I cannot find records that it ever existed. I realized with this tool I could recreate the screen to show other people what it looked like and maybe help me find proof the real one existed. My memory of the real screen itself is foggy, other than that it featured two dragons playing with a ball in a snowy sky, and that it was Japanese, from sometime between 1000–1500.” — P95 | |
| “I started with the idea of a dragon emerging from Peranakan-inspired plates and then played with the idea of water hitting the plates, focusing on the flow of the water symbolizing the ‘washing away’ of culture and then settling on the movement being a surrealist depiction of the plates themselves, withering, washing or slipping away in a sink. I was inspired by a news article in a local paper from home I saw recently about how younger generations tend to forget or reject our culture.” — P61 | |
| “I first started by combining two random interests of mine, coral reefs, and brutalist architecture. Then I picked two of my favorite artists, Jean-Michel Basquiat and Katsushika Hokusai, asking AI to weave in their artistic style to the piece. The piece became more colorful, more interpretive, and the texture slightly changed. The coral was overflowing in weird shapes and sizes just how I wanted, and the style of Hokusai was present in the water around the piece.” — P84 | |
| “I began by referencing the iconic Louise Bourgeois ‘I have been to hell and back’ poster. The most direct inspiration came from my friend’s offhand comment about ‘all these girls and their anxiety.’ This phrase, delivered with care and gentle humor, transformed my personal experience of anxiety into something shared and less isolating. After generating the image, I had it printed and framed, placing it in my bedroom where it would be the first thing I see each morning—transforming my AI creation into a tangible daily affirmation.” — P32 |
Table 1. Sample artworks with creator descriptions of their artistic process and inspirations. Creators drew on diverse sources, from childhood memories of museum artifacts (P95) to contemporary art references (P32), cross-cultural artistic fusion (P84), and personal cultural heritage (P61).
| Author(s), Year | Focus | Method | Variables | Main Findings | Limitations |
|---|---|---|---|---|---|
| Bellaiche et al. (2023) (N=297) | Human vs. AI art preference | Two experimental studies | Human vs. AI source cues, preference ratings | Strong bias toward human-labeled art across all criteria (liking, beauty, profundity, worth), explained by authenticity and perceived effort | All artworks were actually AI-created; used deception about origins |
| Epstein et al. (2020) (N=433) | Authorship credit for AI art | Two experimental studies | AI anthropomorphism, responsibility attribution | Credit allocation affected by how AI is described; anthropomorphic language increases AI responsibility attribution | Pre-generative AI era; vignette-based tasks; no real artworks |
| He et al. (2025) (N=155) | Human–AI co-creation | Online vignette experiment | Contribution types, attribution | Visible human effort increased human credit; opaque effort favored AI | Scenario-based, self-reported |
| Horton et al. (2023) (N=2,965) | Creativity under AI disclosure | 6 online experiments | Creativity ratings, disclosure effects | AI-labeled art judged less creative; human-labeled more creative by contrast | Focus on creativity only; limited valuation |
| Lee et al. (2024) (auction records) | Art market dynamics | Auction data analysis | Social vs. visual features | Social signals (reputation, sales history) influenced prices more than visual features | Established markets only; no AI art |
| Lima et al. (2025) (N=432) | authorship and rights attribution | Juried art competition | Creativity, effort, quality | egocentric effect in authorship and rights attribution, | Held Online exhibition |
Table 2. Literature Comparison. We compiled the most related works, including our prior work, to highlight the unique contributions of this study.
| Code | Definition | Sample Coding |
| Process | ||
| Ideation | Pre-existing ideas prior to AI interaction | “I uploaded the image and gave the rough prompt: try to make an image of the building of the line drawing uploaded, but entirely out of food” (P14) |
| Iteration | Repeated attempts without specifying a problem | “I input over 100 prompts, each one as they occurred to me, and often guided the AI toward or away from decisions it seemed to have made” (P89) |
| AI-Driven | Output primarily determined by model | “I tried to think of some random combinations of objects, themes, and styles to throw at the AI generator to see how it would handle them” (P55) |
| Challenges | ||
| Technical Limitations | Model constraints or artifacts | “The limitations of the image generation model to create images as I described forced me to pivot and adjust my language multiple times” (P2) |
Table 3. Codebook sample for qualitative analysis of creator responses (N = 104). Codes were developed through iterative open coding and organized into five thematic categories.
| Creators: Creativity Perceptions | Evaluators: Creativity Perceptions | |
| (Intercept) | * | |
| ( ) | ( ) | |
| Effort | *** | |
| ( ) | ( ) | |
| Visual appeal | *** | |
| ( ) |
Table 4. Creativity perception models. We model perceived creativity separately for creators and evaluators. Blank cells indicate covariates not included in a given specification. Refer to Section 4.2 for details. Standard errors are shown inside parentheses. ; ; .
| Creators: Pricing | Evaluators: Pricing | |
| (Intercept) | *** | |
| ( ) | ( ) | |
| Creativity | *** | |
| ( ) | ( ) | |
| Effort | *** | |
| ( ) | ( ) |
Table 5. Pricing models. We model pricing separately for creators and evaluators (evaluator mixed-effects model). Refer to Section 4.2 for details. Standard errors are shown inside parentheses. ; ; .
| User (C) | User (E) | Artist (C) | Artist (E) | Tech (C) | Tech (E) | |
| Creativity | ||||||
| ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | |
| Effort | ** | |||||
| ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | |
| Technical quality | ** | |||||
| ( ) | ( ) | ( ) | ( ) | ( ) | ( ) |
Table 6. Prize allocation models. We estimate prize allocation for User, Artist, and Tech entities for: (C) creator and (E) evaluator models. Refer to Section 4.3 for details. Standard errors are shown inside parentheses. ; ; ; .
| User (C) | User (E) | Tech (C) | Tech (E) | Artist (C) | Artist (E) | |
| Creativity | + | |||||
| ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | |
| Effort | *** | + | ||||
| ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | |
| Visual appeal | * | |||||
| ( ) | ( ) | ( ) |
Table 7. Exclusive Rights Attribution Models. Attribution of exclusive rights for Users, Tech entities (Tech), and training-data Artist from creators and evaluators. Standard errors are shown inside parentheses. ; ; ; .
More in this topic
Human-AI Co-Creation
Human-AI co-creation studies how people and AI jointly develop ideas, artifacts, and decisions while preserving meaningful human agency.
See all 160 papers