CoChat
Sign Up
Cited by 25Art, Technology, and CultureDigital Media and Visual ArtCultural Industries and Urban Development

Perceptions and integration of generative artificial intelligence in creative practices and industries: a scoping review and conceptual model

Open access
Tsao, Jack, Liang, Cindy Xinyi, Nogues, Collier, Wong, Alice·2026·AI & SOCIETY
DOI 10.1007/s00146-025-02667-2
Open this paper in CoChat — free accountOpen PDF

Abstract

Generative Artificial Intelligence (GenAI) is fundamentally transforming notions of creativity and creative production across disciplines, yet a comprehensive understanding of professional attitudes and integration patterns remains challenging. This scoping review examines how creative professionals perceive and integrate GenAI technologies across four domains: visual art and design, writing and literature, performing arts, and environmental and spatial design. Following PRISMA-ScR guidelines, we analysed 57 papers (2022–2025) from multiple databases, focusing mainly on empirically based studies of professional creative practice. We identify universal trends, including the shift from creation to curation and meta-creation, the emergence of new literacies (prompt engineering, AI evaluation), and the reconfiguration of expertise hierarchies. Career stage emerges as a critical factor: across domains, entry-level professionals demonstrate enthusiasm and view GenAI as a natural extension of digital tools, while senior practitioners express scepticism about expertise devaluation. All fields consistently position GenAI in early-stage conceptualisation rather than final production, developing hybrid methodologies that preserve human judgment in convergent creative phases. However, impacts manifest differently based on each field/ sub-field’s relationship to embodiment and materiality. Integration levels follow an inverse relationship with traditional notions of “pure” creativity—fields prioritising embodied practice and cultural authenticity (fine arts, literary fiction, classical music) show the most significant resistance. At the same time, commercially oriented domains embrace higher adoption for efficiency gains. Visual artists face “erasure by obscurity” from AI output volume; writers negotiate between nonlinear, associative language generation and AI’s tendency toward coherence and cliché; performers uniquely embrace computational unpredictability as a generative force; architects balance efficiency imperatives with concerns about cultural homogenisation effects of Western-centric AI systems. Our conceptual framework positions creative professionals’ attitudes along two dimensions: knowledge codifiability (tacit/embodied to explicit/codifiable) and output materiality (physical/permanent to digital/ephemeral). Key tensions include balancing productivity with meaningful engagement, volume with distinction, precision with serendipity, and individual with collective intelligence. Ultimately, our findings reveal complex patterns of ambivalence shaped by competing value systems. This review contributes a comprehensive mapping of GenAI applications across creative disciplines, potential disconnects between educational emphasis on critique and industry’s pragmatic adoption, and gaps for future research on longitudinal skill impacts, emerging AI-native practices, Global South perspectives, and forms of creative resistance.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s00146-025-02667-2

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 59 · Cited by 25

Research works printed but not linked: 54

  1. Arksey H, O'Malley L (2005) Scoping studies: towards a methodologi- cal framework. Int J Soc Res Methodol 8(1):19-32. https:// doi. org/ 10. 1080/ 13645 57032 00011 9616 · DOI · Paper page
  2. Atanacković D (2024) Artificial intelligence: duality in applications of generative AI and assistive AI in music. INSAM J Contemp Music Art Technol 12:12-31. https:// doi. org/ 10. 51191/ issn. 2637-1898. 2024.7. 12. 12 · DOI
  3. Bomba F, Menéndez-Blanco M, Grigis P, Cremaschi M, De Angeli A (2024) The choreographer-performer continuum: a diffraction tool to illuminate authorship in more than human co-performances. ACM Trans Comp Hum Inter 31(6):1-23. https:// doi. org/ 10. 1145/ 36890 40 · DOI
  4. Bourdieu P (1993) The field of cultural production: Essays on art and literature. Columbia University Press, Columbia
  5. Braccini M, Filippo AD, Lombardi M, Milano M (2024) Swarm intel- ligence: a novel and unconventional approach to dance choreog- raphy creation. In: Proceedings of the 3rd Workshop on Artificial Intelligence and Creativity Co-Located with 27th European Con- ference on Artificial Intelligence (ECAI 2024), pp 162-172
  6. Brandt A (2021) Defining creativity: a view from the arts. Creat Res J 33(2):81-95. https:// doi. org/ 10. 1080/ 10400 419. 2020. 18559 05 · DOI
  7. Bryan-Kinns N, Noel-Hirst A, Ford C (2024) Using incongruous gen- res to explore music making with AI generated content. In: Pro- ceedings of the 16th Conference on Creativity and Cognition, pp 229-240. https:// doi. org/ 10. 1145/ 36356 36. 36561 98 · DOI
  8. Carrera D, Zhao Z, Thomas A, Wigdor D (2025) Nabokov's cards: an LLM-assisted co-writing system to support bottom-up creative writing. In: Proceedings of the 2025 Conference on Creativity and Cognition, pp 546-559. https:// doi. org/ 10. 1145/ 36980 61. 372691 · DOI
  9. Dake DM (1991) The visual definition of visual creativity. J vis Lit- eracy 11(1):100-104. https:// doi. org/ 10. 1080/ 23796 529. 1991. 11674 461 · DOI
  10. Davis RL, Wambsganss T, Jiang W, Kim KG, Käser T, Dillenbourg P (2023) Fashioning the future: unlocking the creative potential of deep generative models for design space exploration. In: Extended Abstracts of the 2023 CHI Conference on Human Factors in Com- puting Systems, pp 1-9. https:// doi. org/ 10. 1145/ 35445 49. 35856 44 · DOI
  11. El Moussaoui M (2025) Architectural practice process and artificial intelligence-an evolving practice. Open Eng 15(1):20240098. https:// doi. org/ 10. 1515/ eng-2024-0098 · DOI
  12. Fathoni AFCA (2023) Leveraging generative AI solutions in art and design education: Bridging sustainable creativity and foster- ing academic integrity for innovative society. E3S Web Confer. https:// doi. org/ 10. 1051/ e3sco nf/ 20234 26011 02 · DOI
  13. Godart F, Seong S, Phillips DJ (2020) The sociology of creativity: elements, structures, and audiences. Annu Rev Sociol 46(1):489- 510. https:// doi. org/ 10. 1146/ annur ev-soc-121919-054833 · DOI
  14. Green AE, Beaty RE, Kenett YN, Kaufman JC (2024) The process definition of creativity. Creat Res J 36(3):544-572. https:// doi. org/ 10. 1080/ 10400 419. 2023. 22545 · DOI
  15. Guo A, Sathyanarayanan S, Wang L, Heer J, Zhang AX (2025) From pen to prompt: how creative writers integrate AI into their writing practice. In: Proceedings of the 2025 Conference on Creativity and Cognition, pp 527-545. https:// doi. org/ 10. 1145/ 36980 61. 37269 10 · DOI
  16. Haase J, Pokutta S (2024) Human-AI co-creativity: exploring synergies across levels of creative collaboration (Version 2). https:// doi. org/ 10. 48550/ ARXIV. 2411. 12527 · DOI
  17. Hong HT, Teles de Castro e Costa I, Tanguy A, Kheddar A, Chen CY (2024) A cross-temporal robotic dance performance: dancing with a humanoid robot and artificial life. In: ISEA 2024-29th International Symposium on Electronic Art. https:// hal. scien ce/ hal-04755 684
  18. Hu X, Xing Y, Cai X, Zhao Y, Cook M, Borgo R, Neate T (2025) Designing interactions with generative AI for art and creativity: A systematic review and taxonomy. In: Proceedings of the 2025 ACM Designing Interactive Systems Conference, pp 1126-1155. https:// doi. org/ 10. 1145/ 37153 36. 37358 43 · DOI
  19. Jones CA, Gupta H, Ritchie M (2024) Visual artists, technological shock, and generative AI. https:// doi. org/ 10. 21428/ e4bae dd9. b4f75 4fd · DOI
  20. Karimi P, Rezwana J, Siddiqui S, Maher ML, Dehbozorgi N (2020) Creative sketching partner: an analysis of human-AI co-creativity. In: Proceedings of the 25th International Conference on Intelli- gent User Interfaces, pp 221-230. https:// doi. org/ 10. 1145/ 33773 25. 33775 22 · DOI
  21. Keith S (2024) Academic integrity in creative arts and media. In: Bretag T (ed) Second handbook of academic integrity. Springer Nature Switzerland, pp 523-538
  22. Kim S, Oh D (2025) Evaluating creativity: can LLMs be good evalu- ators in creative writing tasks? Appl Sci 15(6):2971. https:// doi. org/ 10. 3390/ app15 062971 · DOI
  23. Latour B (2005) Reassembling the social: an introduction to actor- network-theory. Oxford University Press. https:// doi. org/ 10. 1093/ oso/ 97801 99256 044. 001. 0001 · DOI · Paper page
  24. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta- analyses of studies that evaluate health care interventions: expla- nation and elaboration. PLoS Med 6(7):e1000100. https:// doi. org/ 10. 1371/ journ al. pmed. 10001 00 · DOI · Paper page
  25. Liu L (2024) Academic integrity in digital media art education in the AI era. J High Vocat Educ 1(2):15-21. https:// doi. org/ 10. 62517/ jhve. 20241 6203 · DOI
  26. Loor Paredes M (2025) Emerging paradigms in music technology: valuing mistakes, glitches and uncertainty in the age of gen- erative AI and automation. AI Soc. https:// doi. org/ 10. 1007/ s00146-025-02209-w · DOI
  27. Maher ML, Poon J, Boulanger S (1996) Formalising design exploration as co-evolution. In: Gero JS, Sudweeks F (eds) Advances in for- mal design methods for CAD. Springer, Berlin, pp 3-30. https:// doi. org/ 10. 1007/ 978-0-387-34925-1_1 · DOI
  28. Martin PJ, Sicchio K, Chang G, Dietzel C (2024) A prototype platform for live human-robot choreography. In: Proceedings of the 9th International Conference on Movement and Computing, pp 1-8. https:// doi. org/ 10. 1145/ 36588 52. 36590 71 · DOI
  29. Mirwais M, Adeel M, Rahmani AW, Rahmani AN (2025) Ai-driven generative design for next-generation 3D concrete printing in architecture: state of the art. Eur J Appl Sci Eng Technol 3(2):225-232. https:// doi. org/ 10. 59324/ ejaset. 2025. 3(2). 19 · DOI
  30. Muehlhaus M, Steimle J (2024) Interaction design with generative AI: an empirical study of emerging strategies across the four phases of design. https:// doi. org/ 10. 48550/ arXiv. 2411. 02662 · DOI
  31. Mycka J, Mańdziuk J (2025) Artificial intelligence in music: recent trends and challenges. Neural Comput Appl 37(2):801-839. https:// doi. org/ 10. 1007/ s00521-024-10555-x · DOI
  32. Naqvi SM, He R, Kaur H (2025) Catalyst for creativity or a hollow trend?: A cross-level perspective on the role of generative AI in design. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp 1-16. https:// doi. org/ 10. 1145/ 37065 98. 37132 33 · DOI
  33. Novak-Leonard JL, Skaggs R, Robinson M (2022) Innovative and artis- tic: conceptions of creativity among the American public. Poetics 90:101599. https:// doi. org/ 10. 1016/j. poetic. 2021. 101599 · DOI
  34. Onatayo D, Onososen A, Oyediran AO, Oyediran H, Arowoiya V, Onatayo E (2024) Generative AI applications in architecture, engineering, and construction: trends, implications for practice, education and imperatives for upskilling-a review. Architecture 4(4):877-902. https:// doi. org/ 10. 3390/ archi tectu re404 0046 · DOI
  35. Pataranutaporn P, Mano P, Bhongse-Tong P, Chongchadklang T, Archiwaranguprok C, Hantrakul L, Eaimsa-ard J, Maes P, Klun- chun P (2024) Human-AI co-dancing: Evolving cultural herit- age through collaborative choreography with generative virtual characters. In: Proceedings of the 9th International Conference on Movement and Computing, pp 1-10. https:// doi. org/ 10. 1145/ 36588 52. 36613 17 · DOI
  36. Pelowski M, Markey PS, Forster M, Gerger G, Leder H (2017) Move me, astonish me… delight my eyes and brain: the Vienna inte- grated model of top-down and bottom-up processes in art per- ception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates. Phys Life Rev 21:80-125. https:// doi. org/ 10. 1016/j. plrev. 2017. 02. 003 · DOI
  37. Polanyi M (2009) The tacit dimension (Reproduction en fac-similé). University of Chicago Press
  38. Puryear JS, Lamb KN (2020) Defining creativity: how far have we come since Plucker, Beghetto, and Dow? Creat Res J 32(3):206- 214. https:// doi. org/ 10. 1080/ 10400 419. 2020. 18215 · DOI
  39. Qian P, Hu Q (2024) AI-based English writing quality assessment model and its applications. In: Proceedings of the 5th International Conference on Computer Information and Big Data Applications, pp 809-813. https:// doi. org/ 10. 1145/ 36711 51. 36712 · DOI
  40. Qin HX, Jin S, Gao Z, Fan M, Hui P (2024) CharacterMeet: support- ing creative writers' entire story character construction processes through conversation with LLM-powered chatbot avatars. In: Pro- ceedings of the CHI Conference on Human Factors in Computing Systems, pp 1-19. https:// doi. org/ 10. 1145/ 36139 04. 36421 05 · DOI · Paper page
  41. R'boul H (2022) Communicating creativities: interculturality, postco- loniality and power relations. Third World Q 43(6):1478-1494. https:// doi. org/ 10. 1080/ 01436 597. 2022. 20594 62 · DOI
  42. Rane NL, Choudhary SP, Rane J (2023) Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in architectural design and engineering: applications, framework, and challenges. Int J Archit Plann 3(2):92-124. https:// doi. org/ 10. 51483/ IJARP.3. 2. 2023. 92-124 · DOI
  43. Roncoroni UL, Crousse De Vallongue V, Centurion Bolaños O (2024) Computational creativity issues in generative design and digital fabrication of complex 3D meshes. Int J Archit Comput 23(2):582-600. https:// doi. org/ 10. 1177/ 14780 77124 12608 50 · DOI
  44. Runco MA (2023) AI can only produce artificial creativity. J Creat 33(3):100063. https:// doi. org/ 10. 1016/j. yjoc. 2023. 100063 · DOI
  45. Saliu N, Elezi K (2025) The transformative integration of artificial intelligence in architectural practice: from generative design to sustainable building performance. Eur Chronicle 10(1):66-73. https:// doi. org/ 10. 59214/ euch/1. 2025. · DOI
  46. Shan S, Ding W, Passananti J, Wu S, Zheng H, Zhao BY (2024) Night- shade: prompt-specific poisoning attacks on text-to-image genera- tive models. IEEE Sympos Secur Privacy (SP) 2024:807-825. https:// doi. org/ 10. 1109/ SP542 63. 2024. 00207 · DOI
  47. Sternberg RJ, Lubart TI (1999) The concept of creativity: prospects and paradigms. Handbook of creativity. Cambridge University Press, Cambridge, pp 3-15
  48. Thörn O, Knudsen P, Saffiotti A (2020) Human-robot artistic co-cre- ation: a study in improvised robot dance. In: 2020 29th IEEE International Conference on Robot and Human Interactive Com- munication (RO-MAN), pp 845-850. https:// doi. org/ 10. 1109/ RO- MAN47 096. 2020. 92234 46 · DOI
  49. Uusitalo S, Salovaara A, Jokela T, Salmimaa M (2024) "Clay to play with": generative AI tools in UX and industrial design practice. Design Interact Syst Confer DOI. https:// doi. org/ 10. 1145/ 36438 34. 36616 24 · DOI · Paper page
  50. Venugopal RK (2025) Theatre in the digital age: Modern performance challenges and the influence of AI. Acad Int J Multidiscip Res 3(3):1367-1376. https:// doi. org/ 10. 5281/ ZENODO. 15225 346 · DOI
  51. Wallace B, Nymoen K, Torresen J, Martin CP (2024) Breaking from realism: exploring the potential of glitch in AI-generated dance. Digit Creat 35(2):125-142. https:// doi. org/ 10. 1080/ 14626 268. 2024. 23270 · DOI
  52. Wang T, Chen J, Jia Q, Wang S, Fang R, Wang H, Gao Z, Xie C, Xu C, Dai J, Liu Y, Wu J, Ding S, Li L, Huang Z, Deng X, Yu T, Ma G, Xiao H et al (2024) Weaver: foundation models for creative writing (No. arXiv: 2401. 17268). https:// doi. org/ 10. 48550/ arXiv. 2401. 17268 · DOI
  53. Wingström R, Hautala J, Lundman R (2022) Redefining creativity in the era of AI? Perspectives of computer scientists and new media artists. Creat Res J 36(2):177-193. https:// doi. org/ 10. 1080/ 10400 419. 2022. 21078 · DOI · Paper page
  54. Zhou E, Lee D (2024) Generative artificial intelligence, human creativ- ity, and art. PNAS Nexus 3(3):pgae052. https:// doi. org/ 10. 1093/ pnasn exus/ pgae0 52 · DOI · Paper page

Authors
Tsao, J., Liang, C. X., Nogues, C., & Wong, A.
Affiliations
  • University of Hong Kong (HK)
  • Chinese University of Hong Kong (HK)
Corresponding author
Tsao, Jack
ORCID
Venue
AI & SOCIETY
Publisher
Springer Science and Business Media LLC
Published
2025-10-08
Volume
41
Issue
3
Pages
2259-2278
ISSN
  • 0951-5666 (ISSN-L)
  • 1435-5655
DOI
10.1007/s00146-025-02667-2
OpenAlex
W4414939510
Cited by
25
References
59
Citations by year
2026: 22 · 2025: 3
FWCI
281.67
Type
journal article
Language
en
Open access
Licence
Locations
AI & Society (publishedVersion, cc-by)
Funders
Hong Kong University Grant Committee’s Teaching Development and Language Enhancement Grant
Topics
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
  • Social Sciences › Urban Studies › Cultural Industries and Urban Development
Keywords
generative AI, creative workers, creativity, literature and writing, performing arts, prompt engineering, career stages, embodied practice, materiality, cultural homogenization, creative resistance, conceptual framework

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review