GENERATIVE ARTIFICIAL INTELLIGENCE AS A TOOL OF CONTEMPORARY POPULAR CULTURE: ENCODING OF CULTURAL MEANINGS IN NATIONAL CONTEXTS

Abstract

The rapid expansion of generative artificial intelligence (AI) has significantly transformed contemporary digital culture. While AI is often discussed either as an autonomous creative agent or as a technological tool reshaping cultural industries, less attention has been paid to its role within bottom-up popular culture and nationally conditioned models of meaning production. The problem addressed in the study concerns the cultural mechanisms through which generative AI participates in popular culture without replacing human authorship, instead transforming the processes of encoding and reinterpretation of meanings within specific cultural contexts. The aim and tasks. The aim of the article is to analyze generative AI as a tool of meaning re-encoding in contemporary popular culture and to identify nationally determined models of digital creativity. The tasks include conceptualizing popular culture as a participatory postmodern practice, applying Stuart Hall’s encoding/decoding theory to AI-assisted creativity, and examining mechanisms of primary and secondary encoding; and (4) conducting a comparative analysis of Western and Ukrainian user-generated digital practices. Research methods. The research is based on cultural analysis, comparative methodology, and interpretative analysis grounded in Stuart Hall’s encoding/decoding model. Case studies of user-generated AI content (visual memes and stylistic experiments in Western contexts; AI-assisted musical reinterpretations of Ukrainian poetic canon) serve as empirical material for examining national models of digital meaning production. Research results. The study demonstrates that generative AI functions primarily as a technological mediator rather than as an independent creative subject. Meaning is encoded at the level of prompt construction, stylistic selection, editing, and dissemination. The comparative analysis reveals two distinct national models. In Western digital culture, AI is predominantly used for visual hybridization, intertextual play, and stylistic experimentation. In contrast, Ukrainian digital practices emphasize musical and narrative forms rooted in cultural memory and the reactivation of poetic canon, reflecting a value-oriented model of encoding. These differences indicate that AI technologies integrate into pre-existing symbolic frameworks rather than homogenizing cultural expression. Conclusions. Generative AI transforms the modes of cultural production but does not eliminate human intentionality. National cultural traditions significantly influence encoding strategies and dominant decoding positions in AI-assisted popular culture. The findings confirm that digital technologies operate within culturally specific meaning-making systems, reinforcing rather than erasing national symbolic structures.