Artificial Intelligence (AI) is rapidly transforming various fields, including art. Studies comparing human- and AI-generated art primarily examine evaluation, perception, and the ability to identify art created by AI. To the authors’ knowledge, no study was focused on creating visitor segments based on their AI art perception. To answer this gap, this article presents a comprehensive empirical study examining the integration of AI into a contemporary art exhibition and creating visitor segments. The exhibition featured original paintings by Klára Sedlo, displayed alongside their AI-generated visual reproductions, text-based analyses, and audio interpretations. Employing advanced generative models (GPT-4o, GPT-4o mini, DALL-E 3, and TTS-1), we produced multiple iterations for 19 selected artworks. Visitor perceptions were systematically examined using questionnaires and analyzed by statistical methods, including factor and cluster analysis. The findings reveal a wide spectrum of audience responses regarding the aesthetic and conceptual value of AI-generated art, creating four visitor segments: Traditionalists, Visualists, Skeptics, and AI Enthusiasts. This study highlights key curatorial and audience-acceptance challenges associated with employing AI as a “co-creator” in gallery practice.
Robert Šamárek, Kristýna Klimková, Vojtěch Koňařík et al.· Scientific Reports· 0 citations
The academic publishing ecosystem is a vast, heterogeneous network of works, authors, institutions, journals, and topics. Traditional scientometrics reduces it to isolated tabular indicators (h-index, Impact Factor) that ignore topological context and are not designed to capture coordinated illegitimate practices. Building on our companion review, which proposed graph analysis of publishing integrity, this paper implements that approach. We define a heterogeneous multivariate graph model over OpenAlex open data (seven node types, seven edge types) and a methodology based on projections (citation and co-authorship networks), interpretable structural metrics, community detection, and three screening detectors of anomalous publishing patterns. We deliberately avoid binary classification: detectors return ranked candidates with explicit structural evidence for human assessment. On the institutional corpus of VSB - Technical University of Ostrava (2020-2025) with its one-hop citation neighbourhood, community detection recovers real research groups, centralities identify cross-disciplinary bridges, and the screenings flag dense co-authorship cliques, locally closed citation loops, and thematically isolated venues. On a second, venue-centric corpus with external ground truth (journals delisted by Scopus and DOAJ) and size-matched controls, a naive case-control design yields seemingly strong but spurious detectors (a prominence confound), whereas after matching the only robust signal is the breadth of disciplinary scope (AUC 0.70); an open graph-based prestige measure (PageRank over the journal citation network) tracks a JIF proxy while being an order of magnitude more resistant to citation gaming than count-based indicators. We release the method as the open-source library apnet with a reproducible CLI workflow and a web interface; the analysis runs on commodity hardware in minutes.
Robert Šamárek, R. Martínek· 0 citations
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