Cross-Cultural Affective Design in AI-Generated Art: Developing Culturally-Inclusive Creative AI

This study examines the potential for developing culturally inclusive artificial intelligence to create emotionally engaging visual art on a global scale. The study employed a mixed-methods approach, involving twelve participants from diverse cultural backgrounds, comprising both expert and non-specialist designers. Participants evaluated AI-generated graphics using culturally relevant motifs, typefaces, and colour schemes. Quantitative assessments of cultural authenticity and sensitivity were augmented with qualitative enquiries to examine interpretive frameworks and emotional responses. Because of the small sample, however, the quantitative results were interpreted cautiously and in recognition of the apparent lack of statistical power. The findings indicate that the identification of the utilised components is affected by cultural proximity, professional expertise, and emotional significance among the participants. Design specialists emphasise typographic precision and formal relationships, whereas individuals with native cultural experience prioritise contextuality and iconicity. Emotional authenticity is recognised as a vital criterion in the acceptance or rejection of AI-generated cultural artefacts. The study contends that attaining a culturally inclusive creative AI necessitates a participatory approach that involves cultural practitioners in data curation, model training, and model evaluation. This paradigm for AI-inspired art aims to enhance representation and foster creativity in AI-generated creations through collaborative equity. This entails the creation of more comprehensive annotated datasets and context-aware engines. The findings offer empirically derived insights into the creation of culturally informed, technologically sophisticated AI systems to facilitate more egalitarian and respectful global digital innovation.