Authors : Thisaranie Kaluarachchi; Dr. Manjusri Wickramasinghe

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/2rf6sepa

Scribd : https://tinyurl.com/bddy5xn8

DOI : https://doi.org/10.38124/ijisrt/25dec165

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Abstract : The post-digital era marks a profound shift in the creative disciplines, where human imagination and computational intelligence converge to redefine authorship, aesthetics, and design practice. This research explores this transformation through the lens of WebDraw, a machine learning–driven system for automatic website prototyping. Positioned at the intersection of artificial intelligence, computational creativity, and post-digital design, WebDraw exemplifies how automation can evolve into a form of creative collaboration rather than mere efficiency. By analyzing WebDraw’s architecture, workflows, and cultural implications, the research argues that design in the post-digital condition is characterized by hybrid authorship, distributed among humans, algorithms, and data. The discussion unfolds across theoretical and practical dimensions: first establishing a conceptual foundation in post- digital design and computational creativity; then presenting WebDraw as a case of human–machine co-creation in web design. Aesthetic evaluation, collaboration dynamics, ethical considerations, and sustainability concerns are explored. Based on both quantitative system evaluations and qualitative interviews with web professionals, the research highlights how WebDraw fosters co-creation, democratizes access to design, and challenges traditional notions of origin, authorship, and creative control. Ultimately, the research positions computational design systems as agents of cultural transformation, expanding the boundaries of creativity, reconfiguring professional practice, and calling for new frameworks in design education and ethics. Through the lens of WebDraw, post-digital creativity emerges not as the replacement of human agency but as its extension through intelligent collaboration.

Keywords : Post-Digital Design, Computational Creativity, AI-Driven Website Prototyping, Human–Machine Co-Creation, Creative Artificial Intelligence, Design Automation.

References :

1. Cramer, F. (2015). What is ‘Post-digital’?. In Postdigital aesthetics: Art, computation and design (pp. 12-26). London: Palgrave Macmillan UK.
2. Berry, D. M., & Dieter, M. (2015). Thinking postdigital aesthetics: Art, computation and design. In Postdigital aesthetics: Art, computation and design (pp. 1-11). London: Palgrave Macmillan UK.
3. Manovich, L. (2023). AI aesthetics. Strelka Press.
4. Veale, T., & Cardoso, F. A. (Eds.). (2019). Computational creativity: The philosophy and engineering of autonomously creative systems. Springer.
5. Kaluarachchi, T., & Wickramasinghe, M. (2024). WebDraw: A machine learning-driven tool for automatic website prototyping. Science of Computer Programming, 233, 103056. https://doi.org/10.1016/j.scico.2023.103056
6. Parikka, J. (2023). Operational images: From the visual to the invisual. U of Minnesota Press.
7. Boden, M. A. (2024). The creative mind: Myths and mechanisms (4th ed.). Routledge.
8. Edmonds, E. (2018). The art of interaction: What HCI can learn from interactive art. Morgan & Claypool Publishers.
9. Jandrić, P., Knox, J., Besley, T., Ryberg, T., Suoranta, J., & Hayes, S. (2018). Postdigital science and education. Educational philosophy and theory, 50(10), 893-899.
10. Bridle, J. (2018). New dark age: Technology and the end of the future. Verso Books.
11. Parikka, J. (2020). The anthrobscene (2nd ed.). University of Minnesota Press.
12. Galanter, P. (2019, July). Artificial intelligence and problems in generative art theory. In Proceedings of EVA London 2019. BCS Learning & Development.
13. Bardzell, J., & Bardzell, S. (2016). Humanistic Hci. Interactions, 23(2), 20-29.
14. Mazzone, M., & Elgammal, A. (2019, February). Art, creativity, and the potential of artificial intelligence. In Arts (Vol. 8, No. 1, p. 26). MDPI.
15. López de Mántaras, R. (2016). Artificial intelligence and the arts: Toward computational creativity.
16. Saunders, R. (2012). Towards autonomous creative systems: A computational approach. Cognitive Computation, 4(3), 216-225.
17. Fan, X., Wu, Z., Yu, C., Rao, F., Shi, W., & Tu, T. (2024, May). ContextCam: Bridging context awareness with creative human-AI image co-creation. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-17).
18. Oxman, R. (2017). Thinking difference: Theories and models of parametric design thinking. Design studies, 52, 4-39.
19. Kaluarachchi, T. (2025). From Mock-ups to Code: A Conceptual Synthesis of AI-Driven Automatic Website Generation. International Journal of Innovative Science and Research Technology, 10(11), 305-317, https://doi.org/10.38124/ijisrt/25nov339
20. Creely, E., & Blannin, J. (2025). Creative partnerships with generative AI. Possibilities for education and beyond. Thinking Skills and Creativity, 56, 101727.
21. McCormack, J., Gifford, T., & Hutchings, P. (2019, April). Autonomy, authenticity, authorship and intention in computer generated art. In International conference on computational intelligence in music, sound, art and design (part of EvoStar) (pp. 35-50). Cham: Springer International Publishing.
22. Manovich, L. (2017). Cultural analytics, social computing and digital humanities.
23. Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
24. Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
25. Mulvenna, M., Boger, J., & Bond, R. (2017, September). Ethical by design: A manifesto. In Proceedings of the European Conference on Cognitive Ergonomics (pp. 51-54).
26. Peters, D., Vold, K., Robinson, D., & Calvo, R. A. (2020). Responsible AI—two frameworks for ethical design practice. IEEE Transactions on Technology and Society, 1(1), 34-47.
27. Caires, C. S. (2025). Creative digital intelligence: reshaping art, experience, and heritage in the post-digital era. Journal of Science and Technology of the Arts, 17(1), 9-18.
28. Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. The MIT Press.
29. Hodgson, J. (2019). Post-digital rhetoric and the new aesthetic. Ohio State University Press.
30. Bo, Y., Yu, J., & Zhang, K. (2018). Computational aesthetics and applications. Visual computing for industry, biomedicine, and art, 1(1), 6.
31. Yu, W. F. (2025). AI as a co-creator and a design material: Transforming the design process. Design Studies, 97, 101303.
32. Martindale, C. (2007). Recent trends in the psychological study of aesthetics, creativity, and the arts. Empirical Studies of the Arts, 25(2), 121-141.
33. Greenfield, G. R. (2005, April). Computational aesthetics as a tool for creativity. In Proceedings of the 5th conference on Creativity & cognition (pp. 232-235).
34. Jordanous, A. (2019). Evaluating evaluation: Assessing progress and practices in computational creativity research. In Computational creativity: The philosophy and engineering of autonomously creative systems (pp. 211-236). Cham: Springer International Publishing.
35. Parisi, L. (2013). Contagious Architecture: Computation, Aesthetics, and Space. MIT Press.
36. Schön, D. A. (2017). The reflective practitioner: How professionals think in action. Routledge.
37. Brown, A. (2012). Creative Partnerships with Technology: How creativity is enhanced through interactions with generative computational systems. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 8, No. 4, pp. 14-20).
38. McGregor, S., Wiggins, G. A., & Purver, M. (2014, June). Computational Creativity: A Philosophical Approach, and an Approach to Philosophy. In ICCC (pp. 254-262).
39. Ritchie, G. (2007). Some empirical criteria for attributing creativity to a computer program. Minds and Machines, 17(1), 67-99.
40. Colton, S., & Wiggins, G. A. (2012). Computational creativity: The final frontier? In ECAI 2012 (pp. 21-26). IOS Press.
41. Reader, J. (2021). Postdigital Humans: Algorithmic Imagination? In Postdigital Humans: Transitions, Transformations and Transcendence (pp. 155-168). Cham: Springer International Publishing.
42. Tenner, E. (2015). The design of everyday things by Donald Norman. Technology and Culture, 56(3), 785-787.
43. Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & automation magazine, 19(2), 98-100.
44. Gunkel, D. J. (2020). The machine question: AI, ethics, and moral responsibility in creative contexts. MIT Press.
45. Galanter, P. (2012). Computational aesthetic evaluation: steps towards machine creativity. In ACM SIGGRAPH 2012 Courses (pp. 1-162).
46. Singh, S., Hindriks, K., Heylen, D., & Baraka, K. (2025). A Systematic Review of Human-AI Co-Creativity. arXiv preprint arXiv:2506.21333.
47. Shneiderman, B. (2022). Human-centered AI. Oxford University Press.
48. Edmonds, E. A. (2014). Human computer interaction, art and experience. In Interactive experience in the digital age: evaluating new art practice (pp. 11-23). Cham: Springer International Publishing.
49. Henriksen, D., Creely, E., & Mehta, R. (2022). Rethinking the politics of creativity: Posthumanism, indigeneity, and creativity beyond the western Anthropocene. Qualitative Inquiry, 28(5), 465-475.
50. Oxman, N. (2016). Age of entanglement. Journal of Design and Science.
51. Boden, M. A. (2016). AI: Its nature and future. Oxford University Press.
52. Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). Can: Creative adversarial networks, generating" art" by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.
53. Fry, T. (2017). Design after design, Design Philosophy Papers, 15:2, 99-102, DOI: https://doi.org/10.1080/14487136.2017.1392093
54. Manzini, E. (2015). Design, when everybody designs: An introduction to design for social innovation. MIT press.
55. Fry, T. (2009). Design futuring. University of New South Wales Press, Sydney, 71-77.
56. Chatterjee, D., & Rao, S. (2020). Computational sustainability: A socio-technical perspective. ACM Computing Surveys (CSUR), 53(5), 1-29.
57. Simanowski, R. (2016). Digital humanities and digital media: Conversations on politics, culture, aesthetics and literacy. Open Humanities Press.
58. Bonadio, E., & McDonagh, L. (2020). Artificial intelligence as producer and consumer of copyright works: evaluating the consequences of algorithmic creativity. Intellectual Property Quarterly, 2020(2), 112-137.
59. Tewari, S. (2018). How to Thrive in the Next Economy: Designing Tomorrow’s World Today.
60. Ihde, D. (2009). Postphenomenology and technoscience: The Peking university lectures. Suny Press.
61. Norman, D. A., & Stappers, P. J. (2015). DesignX: complex sociotechnical systems. She Ji: The Journal of Design, Economics, and Innovation, 1(2), 83-106.
62. Foth, M. (2018). Participatory urban informatics: towards citizen-ability. Smart and sustainable built environment, 7(1), 4-19.
63. Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57-69). Chapman and Hall/CRC.
64. Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
65. Braidotti, R. (2019). Posthuman knowledge (Vol. 2). Cambridge: Polity Press.
66. Deibert, R. (2014). DIY citizenship: Critical making and social media. MIT press.
67. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.
68. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2), 3.