When Art Meets Algorithm: Exploring How People Perceive Meaning in Human–AI Collaborative Art
Tim Döring et al.
Abstract
People’s perception of artificial intelligence (AI) in creative fields remains polarized. In art, perceivers often respond negatively to AI collaborations despite some artworks gaining high valuations. We investigate why AI provokes this tension when earlier artistic tools (e.g., brushes or software) did not, focusing on perceived meaning. Existing art perception frameworks were developed in times when tools functioned as passive extensions of an artist’s intent. As AI occupies an ambiguous position between tool and agentic collaborator, novel features of the creation process become central to how meaning is constructed and evaluated. Across four experiments, we explore how key features of the human–AI collaborative process shape perceived meaning and value. We find that elaborate prompts, human application of the final touch, and curatorial selection from multiple AI-generated outputs enhance perceived meaning and value, whereas prompt modality does not exert a systematic effect. We integrate these findings into a framework of human–AI collaborative creation. In doing so, this research advances understanding of the construction of meaning and value in AI-mediated creative work and opens new avenues for research at the intersection of creative industries in general and art specifically, technology, and management.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.