AI-Generated Abstract Expressionism Inspiring Creativity through Ismail A Mageed's Internal Monologues in Poetic Form

Authors

  • Ismail A Mageed * * PhD, AIMMA, IEEE, IAENG, School of Computer Science, AI, and Electronics, Faculty of Engineering and Digital Technologies, University of Bradford, United Kingdom. https://orcid.org/0000-0002-3691-0773
  • Abdul Raheem Nazir Department of Computing University, Sheffield Hallam University, United Kingdom.

https://doi.org/10.48314/apem.v1i1.21

Abstract

Artificial Intelligence (AI) has revolutionized the creative process, allowing for novel ways of artistic expression. This paper focuses on the intersection of Abstract Expressionism and AI-generated imagery, exploring how poetic prompts inspire unique visual interpretations. By utilizing Leonardo AI with a medium contrast and leveraging the cinematic kino model/preset, the research demonstrates how simple poetic phrases can yield profound visual artworks. The study evaluates the quality, creativity, and emotional resonance of AI-generated art, offering insights into the synergy between human creativity and machine intelligence within an Abstract Expressionism framework. The Leonardo AI is applied to Ismail A Mageed’s Internal Monologues in Poetic Form (IMPFs). The paper ends with some potential open problems and concludes with remarks and future research pathways.

Keywords:

Artificial intelligence, Abstract expressionism, Poetry, Creative collaboration, Leonardo AI

References

  1. [1] Jie, P., Shan, X., & Chung, J. (2023). A comparative analysis between< Leonardo. Ai> and< Meshy> as AI texture generation tools. International journal of advanced culture technology, 11(4), 333–339. https://doi.org/10.17703/IJACT.2023.11.4.333
  2. [2] Mezei, P. (2021). From Leonardo to the next rembrandt–the need for AI-pessimism in the age of algorithms. UFITA archiv für medienrecht und medienwissenschaft, 84(2), 390–429. https://doi.org/10.5771/2568-9185-2020-2-390
  3. [3] Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2020). A systematic review of automatic question generation for educational purposes. International journal of artificial intelligence in education, 30, 121–204. https://doi.org/10.1007/s40593-019-00186-y
  4. [4] Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: approach with an instructional design matrix. Sustainability, 15(15), 11524. https://doi.org/10.3390/su151511524
  5. [5] Yildirim, E. (2023). Comparative analysis of leonardo AI, midjourney, and Dall-E: Ai’s perspective on future cities. Urbanizm: journal of urban planning & sustainable development, (28), 82-96. http://dx.doi.org/10.58225/urbanizm.2023-28-82-96
  6. [6] Bajohr, H. (2024). Operative ekphrasis: the collapse of the text/image distinction in multimodal AI. Word & image, 40(2), 77–90. https://doi.org/10.1080/02666286.2024.2330335
  7. [7] Mikalonytė, E. S., & Kneer, M. (2022). Can artificial intelligence make art?: folk intuitions as to whether AI-driven robots can be viewed as artists and produce art. ACM transactions on human-robot interaction (THRI), 11(4), 1–19. https://doi.org/10.1145/3530875
  8. [8] Pošćić, A., & Kreković, G. (2020). On the human role in generative art: A case study of AI-driven live coding. Journal of science and technology of the arts, 12(3), 45–62. https://doi.org/10.34632/jsta.2020.9488
  9. [9] Cetinic, E., & She, J. (2022). Understanding and creating art with AI: review and outlook. ACM transactions on multimedia computing, communications, and applications (TOMM), 18(2), 1–22. https://doi.org/10.1145/3475799
  10. [10] Shen, Y., & Yu, F. (2021). The influence of artificial intelligence on art design in the digital age. Scientific programming, 2021(1), 4838957. https://doi.org/10.1155/2021/4838957
  11. [11] Xu, X. (2024). A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting. Scientific reports, 14(1), 17846. https://doi.org/10.1038/s41598-024-68375-x
  12. [12] Lehikoinen, K., & Tuittila, S. (2024). Arts‐based approaches for futures workshops: creating and interpreting artistic futures images. Futures & foresight science, 6(3), e182. https://doi.org/10.1002/ffo2.182
  13. [13] Dang, H., Mecke, L., Lehmann, F., Goller, S., & Buschek, D. (2022). How to prompt? opportunities and challenges of zero-and few-shot learning for human-AI interaction in creative applications of generative models. ArXiv preprint ArXiv:2209.01390. https://doi.org/10.48550/arXiv.2209.01390
  14. [14] Wang, M., Liu, Y., Liang, X., Huang, Y., Wang, D., Yang, X., Shen, S., Feng, S., Zhang, X., Guan, C., & Zhang, Y. (2024). Minstrel: structural prompt generation with multi-agents coordination for non-AI experts. ArXiv preprint ArXiv:2409.13449. https://doi.org/10.48550/arXiv.2409.13449
  15. [15] Kulkarni, N. D., & Tupsakhare, P. (2024). Crafting effective prompts: enhancing ai performance through structured input design. Journal of recent trends in computer science and engineering (JRTCSE), 12(5), 1–10. https://doi.org/10.70589/JRTCSE.2024.5.1
  16. [16] Park, D., An, G., Kamyod, C., & Kim, C. G. (2023). A study on performance improvement of prompt engineering for generative AI with a large language model. Journal of web engineering, 22(8), 1187–1206. https://doi.org/10.13052/jwe1540-9589.2285
  17. [17] Korzynski, P., Mazurek, G., Krzypkowska, P., & Kurasinski, A. (2023). Artificial intelligence prompt engineering as a new digital competence: analysis of generative AI technologies such as ChatGPT. Entrepreneurial business and economics review, 11(3), 25–37. https://www.ceeol.com/search/article-detail?id=1205908
  18. [18] Giannini, T., & Bowen, J. P. (2023). Generative art and computational imagination: integrating poetry and art. Proceedings of EVA London 2023 (pp. 211–219). BCS learning & development. https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2023.37
  19. [19] Noel, G. P. J. C. (2024). Evaluating AI‐powered text‐to‐image generators for anatomical illustration: A comparative study. Anatomical sciences education, 17(5), 979–983. https://doi.org/10.1002/ase.2336
  20. [20] Dogoulis, P., Kordopatis-Zilos, G., Kompatsiaris, I., & Papadopoulos, S. (2023). Improving synthetically generated image detection in cross-concept settings. Proceedings of the 2nd ACM international workshop on multimedia AI against disinformation (pp. 28–35). Association for computing machinery. https://doi.org/10.1145/3592572.3592846
  21. [21] Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from gan to chatgpt. ArXiv Preprint ArXiv:2303.04226. https://doi.org/10.48550/arXiv.2303.04226
  22. [22] Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024). The effects of artificial intelligence applications in educational settings: challenges and strategies. Technological forecasting and social change, 199, 123076. https://doi.org/10.1016/j.techfore.2023.123076
  23. [23] Chen, S. Y. (2023). Generative AI, learning and new literacies. Journal of educational technology development & exchange, 16(2), 1-19. https://doi.org/10.18785/jetde.1602.01
  24. [24] Wu, C. C., Song, R., Sakai, T., Cheng, W. F., Xie, X., & Lin, S. D. (2019). Evaluating image-inspired poetry generation. Natural language processing and chinese computing (pp. 539–551). Cham: springer international publishing. https://doi.org/10.1007/978-3-030-32233-5_42
  25. [25] Köbis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in human behavior, 114, 106553. https://doi.org/10.1016/j.chb.2020.106553
  26. [26] Liu, B., Fu, J., Kato, M. P., & Yoshikawa, M. (2018). Beyond narrative description: generating poetry from images by multi-adversarial training. Proceedings of the 26th ACM international conference on multimedia (pp. 783–791). New York, NY, USA: association for computing machinery. https://doi.org/10.1145/3240508.3240587
  27. [27] Lyu, Y., Wang, X., Lin, R., & Wu, J. (2022). Communication in human–AI Co-creation: perceptual analysis of paintings generated by text-to-image system. Applied sciences, 12(22), 11312. https://doi.org/10.3390/app122211312
  28. [28] Abu Zaid, R. M. (2024). Poetry between human mindset and generative artificial intelligence: some relevant applications and implications. Bulletin of the faculty of languages & translation, 27(2), 303–332. https://doi.org/10.21608/bflt.2024.399196
  29. [29] Messer, U. (2024). Co-creating art with generative artificial intelligence: implications for artworks and artists. Computers in human behavior: artificial humans, 2(1), 100056. https://doi.org/10.1016/j.chbah.2024.100056
  30. [30] Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., & Gebru, T. (2023). AI art and its impact on artists. Proceedings of the 2023 AAAI/ACM conference on AI, ethics, and society (pp. 363–374). Association for computing machinery. https://doi.org/10.1145/3600211.3604681
  31. [31] Hong, J. W., & Curran, N. M. (2019). Artificial intelligence, artists, and art: attitudes toward artwork produced by humans vs. artificial intelligence. ACM transactions on multimedia computing, communications, and applications (TOMM), 15(2s), 1–16. https://dl.acm.org/doi/abs/10.1145/3326337
  32. [32] Lovato, J., Zimmerman, J. W., Smith, I., Dodds, P., & Karson, J. L. (2024). Foregrounding artist opinions: A survey study on transparency, ownership, and fairness in AI generative art. Proceedings of the AAAI/ACM conference on AI, ethics, and society. ArXiv. https://doi.org/10.48550/arXiv.2401.15497
  33. [33] Mikalonytundefined, E. S., & Kneer, M. (2022). Can artificial intelligence make art? folk intuitions as to whether AI-driven robots can be viewed as artists and produce art. ACM transactions on human-robot interaction, 11(4), 1–19. http://dx.doi.org/10.1145/3530875
  34. [34] Anscomb, C. (2024). AI: artistic collaborator?. AI & society. https://doi.org/10.1007/s00146-024-02083-y
  35. [35] Yadav, M., Kumar, M., Sahoo, A., & Rathnasiri, M. S. H. (2025). AI and the evolution of artistic expression: impacts on society and culture. In transforming cinema with artificial intelligence (pp. 15–36). IGI global scientific publishing. http://dx.doi.org/10.4018/979-8-3693-3916-9.ch002
  36. [36] Hutson, J., & Schnellmann, A. (2023). The poetry of prompts: the collaborative role of generative artificial intelligence in the creation of poetry and the anxiety of machine influence. Global journal of computer science and technology: D, 23(1). https://digitalcommons.lindenwood.edu/faculty-research-papers/462/
  37. [37] Rahmeh, H. (2023). Digital verses versus inked poetry: exploring readers’ response to AI-generated and human-authored sonnets. Sch int j linguist lit, 6(9), 372–382. https://B2n.ir/p10249
  38. [38] Raj, M., Berg, J., & Seamans, R. (2023). Art-ificial intelligence: the effect of AI disclosure on evaluations of creative content. ArXiv preprint ArXiv:2303.06217. https://doi.org/10.48550/arXiv.2303.06217
  39. [39] Shalevska, E. (2024). The digital laureate: examining AI-generated poetry. RATE Issues, Romanian association of teachers of English, 31(1). http://dx.doi.org/10.69475/RATEI.2024.1.1
  40. [40] Stiles, S. (2023). Ars autopoetica: on authorial intelligence, generative literature, and the future of language. In choreomata performance and performativity after AI (pp. 357–378). Chapman and Hall/CRC. https://doi.org/10.1201/9781003312338
  41. [41] Grassini, S., & Koivisto, M. (2024). Artificial creativity? evaluating AI against human performance in creative interpretation of visual stimuli. International journal of human–computer interaction, 1–12. https://doi.org/10.1080/10447318.2024.2345430
  42. [42] Chen, L., Xiao, S., Chen, Y., Sun, L., Childs, P. R. N., & Han, J. (2023). An artificial intelligence approach for interpreting creative combinational designs. Journal of engineering design, 1–28. https://doi.org/10.1080/09544828.2024.2377068
  43. [43] Grilli, L., & Pedota, M. (2024). Creativity and artificial intelligence: A multilevel perspective. Creativity and innovation management, 33(2), 234–247. https://doi.org/10.1111/caim.12580
  44. [44] Al-Zahrani, A. M. (2024). Balancing act: exploring the interplay between human judgment and artificial intelligence in problem-solving, creativity, and decision-making. Igmin research, 2(3), 145–158. https://www.igminresearch.com/articles/html/igmin158
  45. [45] Qamar, M. T., Yasmeen, J., Pathak, S. K., Sohail, S. S., Madsen, D. O., & Rangarajan, M. (2024). Big claims, low outcomes: fact checking ChatGPT’s efficacy in handling linguistic creativity and ambiguity. Cogent arts & humanities, 11(1), 2353984. https://doi.org/10.1080/23311983.2024.2353984
  46. [46] Gilchrist, B. (2022). Poetics of artificial intelligence in art practice:(Mis) apprehended bodies remixed as language. http://sure.sunderland.ac.uk/id/eprint/14711
  47. [47] da Silva, R. S. R., & de Carvalho, A. C. B. (2024). The creation of mathematical poems and song lyrics by (pre-service) teachers-with-AI as an aesthetic experience. Journal of digital life and learning, 4(1), 43–63. https://doi.org/10.51357/jdll.v4i1.279
  48. [48] Guo, A., Sathyanarayanan, S., Wang, L., Heer, J., & Zhang, A. (2024). From pen to prompt: how creative writers integrate AI into their writing practice. ArXiv Preprint ArXiv:2411.03137. https://doi.org/10.48550/arXiv.2411.03137
  49. [49] Strineholm, P. (2021). Exploring human-robot interaction through explainable AI poetry generation.
  50. [50] Chandrashekar, K., & Jangampet, V. D. (2021). Enhancing generative AI precision: adaptive prompt reinforcement learning for high-fidelity applications. International journal of computer engineering and technology (IJCET), 12(1), 81–90. https://iaeme.com/Home/issue/IJCET?Volume=12&Issue=1
  51. [51] Oppenlaender, J., Linder, R., & Silvennoinen, J. (2024). Prompting AI art: An investigation into the creative skill of prompt engineering. International journal of human–computer interaction, 1–23. https://doi.org/10.1080/10447318.2024.2431761
  52. [52] Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). Dreambooth: fine tuning text-to-image diffusion models for subject-driven generation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 22500–22510). arXiv:2208.12242v2. http://dx.doi.org/10.48550/arXiv.2208.12242
  53. [53] Nur, M. D. M., & Hartati, H. (2024). Utilization of leonardo ai in developing teaching materials for islamic religious education students: case study at ftik uin datokarama palu [presentation]. Proceeding of international conference on islamic and interdisciplinary studies (pp. 302–306). https://jurnal.uindatokarama.ac.id/index.php/iciis/article/view/3525
  54. [54] Po, R., Yifan, W., Golyanik, V., Aberman, K., Barron, J. T., Bermano, A., Chan, E., Dekel, T., Holynski, A., Kanazawa, A., Liu, C. K., Liu, L., Mildenhall, B., Nießner, M., Ommer, B., Theobalt, C., Wonka, P., & Wetzstein, G. (2024). State of the art on diffusion models for visual computing. Computer graphics forum, 43(2), e15063. https://doi.org/10.1111/cgf.15063

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Published

2024-12-28

Issue

Vol. 1 No. 1 (2024): Annals of Process Engineering and Management

Section

Articles

How to Cite

AI-Generated Abstract Expressionism Inspiring Creativity through Ismail A Mageed’s Internal Monologues in Poetic Form. (2024). Annals of Process Engineering and Management, 1(1), 33-85. https://doi.org/10.48314/apem.v1i1.21