Identification and Appraisal of AI-Generated vs. Human-Created Artworks (2024 Survey Dataset)

Name: Identification and Appraisal of AI-Generated vs. Human-Created Artworks (2024 Survey Dataset)
Published: 2025-05-06
License: https://creativecommons.org/licenses/by/4.0

Name	File Type	Size	Last Modified
AI Art Survey Dataset.sav	application/x-spss-sav	758.7 KB	05/06/2025 04:43:AM

Project Citation:

Cunningham, Joshua. Identification and Appraisal of AI-Generated vs. Human-Created Artworks (2024 Survey Dataset). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-05-06. https://doi.org/10.3886/E228723V1

Project Description

Summary: This dataset supports the doctoral dissertation The AI of the Beholder: A Quantitative Study on Human Perception and Appraisal of AI-Generated Images by Joshua Cunningham (Robert Morris University, 2025). The study investigates how individuals perceive and appraise artwork generated by artificial intelligence (AI) in comparison to human-created pieces. Specifically, it examines: (1) whether participants can accurately distinguish AI-generated from human-created artwork, (2) how age and exposure to AI art influence this ability and related appraisals, and (3) how digital versus traditional visual styles of AI art are perceived. The dataset includes anonymized survey responses collected from a diverse group of adult participants. Respondents were asked to evaluate a series of visual artworks—some created by humans, others by AI—across a range of styles, including both digital and traditional aesthetics. Additional demographic information such as age and prior exposure to AI tools was collected to assess moderating effects. The data were analyzed using SPSS to evaluate participant accuracy, preferences, and perceptions. This dataset can support further research into the psychological, aesthetic, and cultural dynamics of AI-generated content, as well as human-machine interaction in the creative arts.

Scope of Project

Subject Terms: Artificial Intelligence; art; information systems; digital communications; survey

Geographic Coverage: United States

Time Period(s): 6/1/2024 – 6/10/2024 (June 2024)

Universe: Adults aged 18+ residing in the United States.

Data Type(s): survey data

Methodology

Response Rate: The sample consisted of U.S.-based adults (N = 406), recruited via CloudResearch’s Connect platform. Connect was selected for its high data quality standards, employing both technical and behavioral measures to screen participants and prevent duplicate accounts, automation, and fraudulent activity (Hartman et al., 2023). Participants were required to be 18 or older, reside in the United States, and complete all survey components.Initial engagement with the survey totaled 510 responses. However, to ensure a high-quality dataset, responses were excluded if participants failed an attention check, skipped questions, or were flagged by SurveyMonkey’s machine learning-based Response Quality tool for issues such as speeding or nonsensical answers. After these exclusions, 406 valid responses remained.

Sampling: A non-probability convenience sample was drawn from U.S.-based adults (aged 18+) using CloudResearch’s Connect platform. Participants were recruited online and screened for eligibility, with technical and behavioral quality checks applied to ensure valid responses.

Scales: A 6-item appraisal scale adapted from Hong and Curran (2019) was used to assess participants' perception of artistic quality. Responses were collected on a 5-point Likert scale ranging from "Strongly disagree" to "Strongly agree.

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Published Versions

V1 [2025-05-06]

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