Measuring Value in AI Generated Visual Art: A Structured Methodological Review of Valuation Measures
- 1Independent Researcher, Kunming, Yunnan, China
- 2Guangzhou Medical University, China
Abstract
Preprint; not peer reviewed. This repository copy reproduces the manuscript first posted on Preprints.org on 21 October 2026 and uses its existing DOI: 10.20944/preprints202610.2051.v1.
Abstract
Studies of AI-generated visual art use value to describe aesthetic ratings, price judgments and acquisition decisions. A synthesis based on these labels alone can combine responses with different meanings. This structured methodological review examines the instruments behind those labels in 12 selected empirical reports from 2018–2026. Targeted searches, reference checking and examination of public methods, supplements and a codebook yielded 25 study units and 46 outcome-family records. Classification distinguished question content, response format and financial consequences, with separate records for source attribution and the object evaluated. Seven study units included economic wording answered without currency; five elicited sale-price judgments, five personal monetary willingness to pay, four purchase intentions or relative buying preferences, and two consequential auction bids. Categories overlap, including within mixed composites. Close reading identified scaled payment questions, a selling-price prediction described as willingness to pay, and an aesthetic-value composite containing economic items. Both auction samples came from one report. The contribution is a classification linked to source passages and a set of reporting fields that preserve these distinctions. For synthesis, a change in rated worth, a recommended or predicted selling price and a consequential bid should be interpreted as different outcomes. For new experiments, the question, response unit and acquisition rule need to be specified together.
Full text and research materials
Original preprint · Whispart Research: full text and research materials.
The deposit includes the manuscript PDF, supplementary methods PDF and supplementary evidence archive.
Disclosure
The manuscript was developed with substantial OpenAI Codex assistance in research, analysis and writing. Whispart covered the preparation costs. Liyuantan is its founder and lichnagtan is a team member. Full contributions, competing interests and methodological limitations are disclosed in the manuscript.
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BibTeX
@misc{tan2026measuring,
title = {{Measuring Value in AI Generated Visual Art: A Structured Methodological Review of Valuation Measures}},
author = {Liyuan Tan and Lichnag Tan},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.20944/preprints202610.2051.v1},
url = {https://zenodo.org/records/23636398},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Measuring Value in AI Generated Visual Art: A Structured Methodological Review of Valuation Measures
AU - Liyuan Tan
AU - Lichnag Tan
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.20944/preprints202610.2051.v1
UR - https://zenodo.org/records/23636398
AB - Preprint; not peer reviewed. This repository copy reproduces the manuscript first posted on Preprints.org on 21 October 2026 and uses its existing DOI: 10.20944/preprints202610.2051.v1. Abstract Studies of AI-generated visual art use value to describe aesthetic ratings, price judgments and acquisition decisions. A synthesis based on these labels alone can combine responses with different meanings. This structured methodological review examines the instruments behind those labels in 12 selected empirical reports from 2018–2026. Targeted searches, reference checking and examination of public methods, supplements and a codebook yielded 25 study units and 46 outcome-family records. Classification distinguished question content, response format and financial consequences, with separate records for source attribution and the object evaluated. Seven study units included economic wording answered without currency; five elicited sale-price judgments, five personal monetary willingness to pay, four purchase intentions or relative buying preferences, and two consequential auction bids. Categories overlap, including within mixed composites. Close reading identified scaled payment questions, a selling-price prediction described as willingness to pay, and an aesthetic-value composite containing economic items. Both auction samples came from one report. The contribution is a classification linked to source passages and a set of reporting fields that preserve these distinctions. For synthesis, a change in rated worth, a recommended or predicted selling price and a consequential bid should be interpreted as different outcomes. For new experiments, the question, response unit and acquisition rule need to be specified together. Full text and research materials Original preprint · Whispart Research: full text and research materials. The deposit includes the manuscript PDF, supplementary methods PDF and supplementary evidence archive. Disclosure The manuscript was developed with substantial OpenAI Codex assistance in research, analysis and writing. Whispart covered the preparation costs. Liyuantan is its founder and lichnagtan is a team member. Full contributions, competing interests and methodological limitations are disclosed in the manuscript.
ER -