Emotional Depth in Generative Imagery: Memory, Trauma, and the Aesthetics of AI

Against the backdrop of the rapid expansion of the generative art market and the associated avalanche-like growth of visual content, there is an intensified demand for updated theoretical foundations for its critical interpretation. The aim of the study is to formulate and substantiate an aesthetic paradigm that shifts the focus from technological novelty to the affective richness of the work and human intentionality. The methodological framework combines a systematic review of academic research on artificial aesthetics and authorship theory, a philosophical analysis of key concepts, and an in-depth case study of an artistic practice that explores themes of memory and trauma using Artificial Intelligence (AI). Based on the analysis, the concept of hybrid authorship is proposed, treating AI as a higher-order instrument and foregrounding affective depth and aesthetic resonance as central evaluative criteria. The study demonstrates that AI can function as an apparatus for the aesthetic materialization of personal and collective experience, particularly in the logic of algorithmic postmemory. The main conclusion confirms the decisive role of authorial intent and the artist’s emotional engagement as necessary conditions for creating meaningful generative art that is distinct from superficial decorative generativity. The findings presented in this work will be of interest to other researchers in the field of art, practicing artists, as well as authors in the areas of digital humanities and the philosophy of technology.