Beyond Human Authorship: Exploring Computational Creativity and Machine-Led Aesthetics

Abstract

The integration of artificial intelligence into artistic practice has transformed computational creativity into a dynamic field where human intuition and algorithmic processes converge. This narrative review examines how human–AI collaboration reshapes creativity, highlighting both opportunities and challenges. A systematic literature search was conducted across databases including Scopus, IEEE Xplore, Google Scholar, and the ACM Digital Library, using keywords such as “computational creativity,” “generative art,” and “human–machine collaboration.” Inclusion criteria prioritized peer-reviewed studies from the last decade, with attention to both technical innovations and socio-cultural dimensions. Findings reveal that collaborative approaches between humans and AI yield more complex and innovative artistic outcomes, supported by techniques such as neural painting and generative music models. Empirical studies demonstrate increasing acceptance of AI-generated art, although biases remain, as audiences often perceive human-made works more favorably. Results also underscore the influence of individual expertise, social interactions, and technological infrastructure on creative processes. Cross-cultural comparisons highlight disparities, with greater acceptance and infrastructure in Europe and North America, contrasted with limited access and cultural ambivalence in developing regions. Discussion points to systemic factors, including policy and education, as critical determinants of adoption and trust. The review concludes that advancing computational creativity requires inclusive access to AI tools, public education to reduce skepticism, and multidimensional evaluation metrics. Future research should expand global perspectives and integrate psychological and cultural frameworks, ensuring equitable participation in the evolving landscape of AI-mediated art.