Where is my Glass Slipper? AI, Poetry and Art
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Abstract (arXiv)
This literature review interrogates the intersections between artificial intelligence, poetry, and art, offering a comprehensive exploration of both historical evolution and current debates in digital creative practices. It traces the development of computer-generated poetry from early template-based systems to generative models, critically assessing evaluative frameworks such as adaptations of the Turing Test, the FACE model, and ProFTAP. It also examines how these frameworks endeavour to measure creativity, semantic coherence, and cultural relevance in AI-generated texts, whilst highlighting the persistent challenges in replicating the nuance of human poetic expression. The review contributes a Marketing Theory discussion that deconstructs the figurative marketing narratives employed by AI companies, which utilise sanitised language and anthropomorphic metaphors to humanise their technologies. This discussion reveals the reductive nature of such narratives and underscores the tension between algorithmic precision and the realities of human creativity.The review also incorporates an auto-ethnographic account that offers a self-reflexive commentary on its own composition. By acknowledging the use of AI in crafting this review, the auto-ethnographic account destabilises conventional notions of authorship and objectivity, resonating with deconstruction and challenging logocentric assumptions in academic discourse. Ultimately, the review calls for a re-evaluation of creative processes that recognises the interdependence of technological innovation and human subjectivity. It advocates for interdisciplinary dialogue addressing ethical, cultural, and philosophical concerns, while reimagining the boundaries of artistic production.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 65 references that are in the map. First posted on arXiv 26 Feb 2025.
Builds on
7 of 65 references.
References · 7
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2017CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Normscited by 96
- 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
- 2020Biases in Generative Art -- A Causal Look from the Lens of Art Historycited by 26
- 2023Is AI Art Another Industrial Revolution in the Making?cited by 5
- 2019AI in the media and creative industriescited by 3
- 2022Environmental and Social Sustainability of Creative-Aicited by 3
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