{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T16:59:29Z","timestamp":1790873969783,"version":"4.1.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Recent advances in generative image models have increased the visual plausibility of AI-generated paintings, raising questions about how viewers attribute image source and whether source-classification performance varies with art-education background. This study examined stimulus-specific patterns of AI\/human source classification, differences associated with art education, and participants\u2019 self-reported visual judgment cues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 356 participants completed a pairwise source-classification task involving six matched pairs of documented human-made paintings and AI-generated counterparts. Participant-level and stimulus-level classification accuracy were evaluated against the 0.50 chance level, and differences among three art-education groups were examined using Welch\u2019s ANOVA and Holm-adjusted pairwise comparisons. Self-reported visual judgment cues were analyzed among 245 participants who provided complete cue rankings. A supplementary eye-tracking component involving 60 questionnaire participants provided archived group-level heatmaps as qualitative visual context only.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall participant-level classification accuracy was modestly but significantly above chance (\n                      <jats:italic>M<\/jats:italic>\n                      \u202f=\u202f0.549, 95% CI [0.525, 0.573]),\n                      <jats:italic>t<\/jats:italic>\n                      (355)\u202f=\u202f4.081,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001, Cohen\u2019s\n                      <jats:italic>d<\/jats:italic>\n                      \u202f=\u202f0.216). However, performance varied substantially across stimuli. Pairs 1, 3, and 6 were classified significantly above chance, Pair 4 did not differ from chance, and Pairs 2 and 5 were significantly below chance. For Pairs 2 and 5, 60.1% and 67.4% of participants, respectively, selected the human-made painting as AI-generated, indicating systematic reversal of source attribution. Art-education background was significantly associated with classification performance, Welch\u2019s\n                      <jats:italic>F<\/jats:italic>\n                      (2, 128.292) = 10.135,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001, but only the some-art-background group performed significantly above chance. Among participants with valid cue rankings, facial and hand details were commonly assigned higher priority, whereas background details were most frequently ranked last; neither first- nor last-ranked cue distributions differed significantly across art-education groups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>AI\/human painting source classification was characterized by marked stimulus-specific directionality rather than a uniform level of discrimination difficulty. The below-chance performance observed for two stimulus pairs indicates systematic source-attribution reversal, while the art-education results do not support a simple monotonic expertise advantage. These findings suggest that source judgments depend on interactions among stimulus-specific visual properties, viewers\u2019 art-education backgrounds, and reported judgment strategies rather than on a single general ability to distinguish AI-generated from human-made paintings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1903106","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T16:07:52Z","timestamp":1790870872000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Judging AI-generated paintings: art-education differences in source classification"],"prefix":"10.3389","volume":"17","author":[{"given":"Yi","family":"Huang","sequence":"first","affiliation":[{"name":"School of Art, Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyue","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Art, Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Art, Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1648480","DOI":"10.3389\/fpsyg.2025.1648480","article-title":"Creativity and aesthetic evaluation of AI-generated artworks: bridging problems and methods from psychology to AI","volume":"16","author":"Bianchi","year":"2025","journal-title":"Front. 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