2026-08-21T10:55:07.349Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:12146 2026-08-13T10:18:35Z 1

Using the Uniqueness Quotient in the Assessment of Creativity of Image Generation Models. 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/12146 https://portal.sinteza.singidunum.ac.rs/paper/1120 M. Milošević I. Ristić M. Milošević The rapid rise of Generative AI raises questions about its creative abilities, but most assessments focus on text or subjective visual reviews. This study uses the uniqueness quotient to compare the creativity of visuals generated by AI models (Intent-driven, Prompt-driven) and by humans (art and non-art students). Results show no significant difference in overall creativity between humans and AI. Subsample analysis finds Intent-driven models do not differ from art students in originality and outperform non-art students. Prompt- driven models show unique originality patterns. Distribution analysis shows asymmetry: AI models keep a high average, but peak innovativeness remains unique to humans. This study's theoretical contribution is establishing the uniqueness quotient as a reliable, objective method for evaluating visual creativity. In practice, context-aware, intent-driven models can be integrated as advanced co-creators in creative industries, and the uniqueness quotient can serve as a measure of future machine creativity. conferenceObject 240 246 https://doi.org/10.15308/Sinteza-2026-240-246 Sinteza 2026