- Brief created: 2025
- For policymakers
Beyond magic: Prompting for style as affordance actualization in visual generative media
Based on:
Journal Article (2024) ↗
This research studied how people who generate AI images add style modifiers to their prompts and examined what this reveals about how people interact with generative AI.
Brief by:
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This study examined how users of the visual generative AI system Midjourney “prompt for style” to achieve desired aesthetic outcomes, through user discussions on the Midjourney’s Discord server. I did this to explore the dynamics of human-model interaction and discuss the implications of user prompting practices for style recontextualization without proper attribution.
Key findings
Style modifiers function as an entry point into understanding human-model interaction, positioning AI image generation as a complex process of affordance actualization rather than simply magical or deterministic.
Evidence
This means that using style modifiers is not just a technical command but a key part of the interaction between the user and the AI system, where the user’s goals shape the final output. AI image generation is presented as a process of realizing possibilities, rather than a simple, predictable result.
What it means
The paper frames style modifiers as an affordance that users actualize to describe how people perceive and utilize the technological possibilities offered by the AI system. A human prompter acts as a goal-oriented actor, perceiving a style modifier as an opportunity to be acted upon to achieve a specific stylistic outcome.
While visual generative media offers potential for expanding creative expression, the practice of prompting for style often results in generating visual aesthetics that mimics existing cultural artifacts.
Evidence
Style modifiers allow users to mimic the style of human artists, serving as shortcuts to achieving desired visual outputs through associations made from training data containing copyrighted artworks scraped from the web. For example, users can reference names of specific artists to reproduce characteristic styles.
What it means
This means that AI image generation operates through “procedural imitation” of existing works, where the model looks for statistical patterns from its training data. Consequently, what appears to be a creative output is actually a “form of aesthetic mimicry.”
Proposed action
AI image generation platforms must implement clear transparency systems that automatically identify and credit the artistic styles or cultural works being replicated in generated outputs.
There should be more focus on the development of alternative prompting frameworks that encourage creative exploration rather than simple replication of the styles of the living artists.
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Beyond magic: Prompting for style as affordance actualization in visual generative media
Cite this brief: Laba, Nataliia. 'Beyond magic: Prompting for style as affordance actualization in visual generative media'. Acume. https://www.acume.org/r/beyond-magic-prompting-for-style-as-affordance-actualization-in-visual-generative-media/
Brief created by: Dr Nataliia Laba | Year brief made: 2025
Original research:
- Laba, N., ‘Beyond magic: Prompting for style as affordance actualization in visual generative media’ https://doi.org/10.1177/14614448241286144. – https://doi.org/10.1177/14614448241286144
Research brief:
This research studied how people who generate AI images add style modifiers to their prompts and examined what this reveals about how people interact with generative AI.
This study examined how users of the visual generative AI system Midjourney “prompt for style” to achieve desired aesthetic outcomes, through user discussions on the Midjourney’s Discord server. I did this to explore the dynamics of human-model interaction and discuss the implications of user prompting practices for style recontextualization without proper attribution.
Findings:
Style modifiers function as an entry point into understanding human-model interaction, positioning AI image generation as a complex process of affordance actualization rather than simply magical or deterministic.
This means that using style modifiers is not just a technical command but a key part of the interaction between the user and the AI system, where the user’s goals shape the final output. AI image generation is presented as a process of realizing possibilities, rather than a simple, predictable result.
The paper frames style modifiers as an affordance that users actualize to describe how people perceive and utilize the technological possibilities offered by the AI system. A human prompter acts as a goal-oriented actor, perceiving a style modifier as an opportunity to be acted upon to achieve a specific stylistic outcome.
While visual generative media offers potential for expanding creative expression, the practice of prompting for style often results in generating visual aesthetics that mimics existing cultural artifacts.
Style modifiers allow users to mimic the style of human artists, serving as shortcuts to achieving desired visual outputs through associations made from training data containing copyrighted artworks scraped from the web. For example, users can reference names of specific artists to reproduce characteristic styles.
This means that AI image generation operates through “procedural imitation” of existing works, where the model looks for statistical patterns from its training data. Consequently, what appears to be a creative output is actually a “form of aesthetic mimicry.”
Advice:
AI image generation platforms must implement clear transparency systems that automatically identify and credit the artistic styles or cultural works being replicated in generated outputs.
- First, AI companies should collaborate with art historians and copyright lawyers to develop a database of artistic styles and artists’ names that can be automatically detected in AI-generated outputs. Second, AI companies should integrate this detection system into the user interface of platforms like Midjourney, displaying attribution information and suggesting alternative creative approaches when style mimicry is detected. Third, AI companies should establish partnerships with artistic communities and cultural institutions to ensure proper recognition and compensation mechanisms for artists whose styles are being referenced.
There should be more focus on the development of alternative prompting frameworks that encourage creative exploration rather than simple replication of the styles of the living artists.
- First, constraint-based prompting systems that guide users toward original expression should be designed and implemented. This involves developing prompting templates that focus on abstract concepts, emotional states, sensory experiences, and conceptual combinations rather than referencing specific artists or established styles. Second, collaborative educational workshops and community challenges could be organized. This step involves partnering with art educators and experimental artists to develop structured learning programs that demonstrate how to construct prompts using evaluative describers and metaphorical thinking.