SavedAdd your journal

Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2025 / Cilt: 8 Sayı: 2

Enhancing Industrial Product Aesthetics, Ergonomics, and Usability With Artificial Intelligence-Driven Generative Design

Yapay Zekâ Destekli Üretken Tasarımla Endüstriyel Ürün Estetiği, Ergonomisi ve Kullanılabilirliğinin Geliştirilmesi

Hüseyin Özkal Özsoy(MİMAR SİNAN GÜZEL SANATLAR ÜNİVERSİTESİ)0000-0001-5531-3539
Pages
141–155
Publication Date
2025-09-29
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that AI-generated designs improve visual appeal and contribute to a more intuitive user experience.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Generative design is an AI-driven process that utilizes algorithms to generate, evaluate, and optimize multiple design solutions based on predefined constraints.
📊
Key Findings & Data:To achieve this, a mixed-methods approach was adopted, incorporating a literature review, workshop study, and an investigation of user feedback gathered from 30 participants, including industrial design students, engineering students, and users.
💡
Conclusion & Impact:The findings indicate that AI-generated designs improve visual appeal and contribute to a more intuitive user experience.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Endüstriyel Ürün Tasarımı · Tasarım Optimizasyonu · üretken tasarım

Abstract

Generative design is an AI-driven process that utilizes algorithms to generate, evaluate, and optimize multiple design solutions based on predefined constraints. This study explores the impact of AI-driven generative design on home appliances' aesthetics, ergonomics, and usability. To achieve this, a mixed-methods approach was adopted, incorporating a literature review, workshop study, and an investigation of user feedback gathered from 30 participants, including industrial design students, engineering students, and users. These participants evaluated AI-generated designs from their perspectives, focusing on visual appeal, comfort, and ease of use. Generative design software was used to investigate alternative design solutions, such as product forms and control placement positions. The findings indicate that AI-generated designs improve visual appeal and contribute to a more intuitive user experience. However, it was observed that AI-generated designs occasionally prioritized aesthetics over practicality, leading to usability concerns and requiring further refinement to align with real-world manufacturing constraints. The study concludes that while generative design is a valuable tool for enhancing home appliance design, its effectiveness depends on balancing AI-driven optimization with practical considerations.

Özet

Üretken tasarım, önceden tanımlanmış kısıtlamalara dayanarak çoklu tasarım çözümleri üreten, değerlendiren ve optimize eden algoritmalar kullanan yapay zekâ destekli bir süreçtir. Bu çalışma, yapay zekâ destekli üretken tasarımın ev aletlerinin estetiği, ergonomisi ve kullanılabilirliği üzerindeki etkisini araştırmaktadır. Bu amacı gerçekleştirmek için, literatür taraması, atölye çalışması ve 30 katılımcıdan (endüstriyel tasarım öğrencileri, mühendislik öğrencileri ve son kullanıcılar) elde edilen kullanıcı geri bildirimlerinin incelendiği karma yöntemli bir yaklaşım benimsenmiştir. Katılımcılar, görsel çekicilik, konfor ve kullanım kolaylığına odaklanarak YZ tarafından üretilen tasarımları kendi bakış açılarıyla değerlendirmiştir. Ürün formları ve kontrol yerleşimleri gibi alternatif tasarım çözümlerini incelemek amacıyla üretken tasarım yazılımı kullanılmıştır. Bulgular, yapay zekâ ile üretilen tasarımların görsel çekiciliği artırdığını ve daha sezgisel bir kullanıcı deneyimine katkıda bulunduğunu göstermektedir. Ancak, YZ tarafından üretilen tasarımların zaman zaman estetiği işlevselliğin önüne koyduğu, bu nedenle kullanılabilirlik sorunlarına yol açtığı ve gerçek dünya üretim kısıtlarıyla uyum sağlamak için ek düzenlemeler gerektirdiği gözlemlenmiştir. Çalışma, üretken tasarımın ev aletleri tasarımını geliştirmek için değerli bir araç olduğunu; ancak etkinliğinin, YZ destekli optimizasyon ile pratik gereksinimler arasında kurulacak dengeye bağlı olduğunu ortaya koymaktadır.

🔖

Cite This Article

Özsoy, H. Ö. (2025). Enhancing Industrial Product Aesthetics, Ergonomics, and Usability With Artificial Intelligence-Driven Generative Design. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 8 Sayı: 2, 141–155. https://doi.org/10.38016/jista.1677535

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (54)

  1. Agboola, O.P., 2024. The Role of Artificial Intelligence in Enhancing Design Innovation and Sustainability. Smart Des. Policies 1, 6–14. https://doi.org/10.38027/smart-v1n1-2
  2. Badke-Schaub, P., Eris, O., 2014. A Theoretical Approach to Intuition in Design: Does Design Methodology Need to Account for Unconscious Processes?, in: Chakrabarti, A., Blessing, L.T.M. (Eds.), An Anthology of Theories and Models of Design: Philosophy, Approaches and Empirical Explorations. Springer, London, pp. 353–370. https://doi.org/10.1007/978-1-4471-6338-1_17
  3. Balakrishnan, A., Najana, M., 2024. AI-Powered Creativity and Date-Driven Design. https://doi.org/10.2139/ssrn.4907300
  4. Balamurugan, M., Ramamoorthy, L., 2025. Transformative Intelligence: AI and Generative Models as Catalysts For Creative Problem-Solving in Complex Environments 7, 7. https://doi.org/10.36948/ijfmr.2025.v07i02.38404
  5. Batterton, K.A., Hale, K.N., 2017. The Likert Scale What It Is and How To Use It. Phalanx 50, 32–39.
  6. Boggs, C., 2010. Mock-ups in design: the implications of utlizing [sic] a mock-up review process in professional practice. FIU Electron. Theses Diss. https://doi.org/10.25148/etd.FI14051179
  7. Burlin, C., 2023. Explainability to enhance creativity: A human-centered approach to prompt engineering and task allocation in text-to-image models for design purposes.
  8. Channi, H.K., Kaur, A., Kaur, S., 2025. AI-Driven Generative Design Redefines the Engineering Process, in: Generative Artificial Intelligence in Finance. John Wiley & Sons, Ltd, pp. 327–359. https://doi.org/10.1002/9781394271078.ch17
  9. Chukwunweike, J.N., Adebayo, D., Agosa, A.A., Safo, N.O., 2024. Implementation of MATLAB image processing and AI for real-time mood prediction. World J. Adv. Res. Rev. 23, 2599–2620. https://doi.org/10.30574/wjarr.2024.23.1.2258
  10. Dasaka, S., 2024. Optimizing decision-making: Balancing intuition with evidence in digital experience design [WWW Document]. URL https://summit.sfu.ca/item/38179 (accessed 4.7.25).
  11. De Onate, J.M.D., 2024. Industrial Design and AI: how generative artificial intelligence can help the designer in the early stages of a project.
  12. Dutta Majumder, D., 1988. A unified approach to artificial intelligence, pattern recognition, image processing and computer vision in fifth-generation computer systems. Inf. Sci. 45, 391–431. https://doi.org/10.1016/0020-0255(88)90013-8
  13. Fabia, L.-Y.L., 2018. Using Thematic Analysis to Facilitate Meaning‐Making in Practice‐Led Art and Design Research. Int. J. ArtDesign Educ. 38, 153–167.
  14. Garbarino, S., Holland, J., 2009. Quantitative and Qualitative Methods in Impact Evaluation and Measuring Results [WWW Document]. URL http://www.gsdrc.org/docs/open/EIRS4.pdf (accessed 4.7.25).
  15. Ghorbani, M.A., 2024. AI Tools to Support Design Activities and Innovation Processes (laurea). Politecnico di Torino. https://doi.org/10/1/tesi.pdf>
  16. Gowda, R., Poojary, B.V., Sharma, M., Prakash, K., Gowda, N., H, C., 2019. Artificial Intelligence based facial recognition for Mood Charting among men on life style modification and it’s correlation with cortisol. Asian J. Psychiatry 43, 101–104. https://doi.org/10.1016/j.ajp.2019.05.017
  17. Hughes, R.T., Zhu, L., Bednarz, T., 2021. Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends. Front. Artif. Intell. 4. https://doi.org/10.3389/frai.2021.604234
  18. Karlsson, K., Alfgården, H., 2024. Robust concept development utilising artificial intelligence and machine learning.
  19. Keskar, A., 2024. Driving Operational Excellence in Manufacturing through Generative AI: Transformative Approaches for Efficiency, Innovation, and Scalability. Intrnational J. Res. Anal. Rev. 11, 245–261.
  20. Khan, S., Awan, M.J., 2018. A generative design technique for exploring shape variations. Adv. Eng. Inform. 38, 712–724. https://doi.org/10.1016/j.aei.2018.10.005
  21. Khor, K.C., 2023. Physical Prototyping — A1: Model Prototype. Medium. URL https://medium.com/@kkhor01/hcde-451-a1-model-prototype-8cc954483fa9 (accessed 4.3.25).
  22. Kulkarni, N., Tupsakhare, P., 2024. Crafting Effective Prompts: Enhancing AI Performance through Structured Input Design. J. Recent Trends Comput. Sci. Eng. 12, 1–10. https://doi.org/10.70589/JRTCSE.2024.5.1
  23. Κυρτσίδου, Α.Χ., 2024. Navigating the digital disruption: agile strategies for regulated global industries in the era of rapid technological changes in international business.
  24. Lehtimäki, J., 2024. AI-assisted social media content creation workflow (MSc.). Turku University of Applied Sciences, Turku.
  25. Lei, D.T., 2000. Industry evolution and competence development: the imperatives of technological convergence. Int. J. Technol. Manag. 19, 699–738. https://doi.org/10.1504/IJTM.2000.002848
  26. Lopez, D., Bhutto, F., 2023. Human-Centered Design in Product Development: A Paradigm Shift for Innovation. Abbottabad Univ. J. Bus. Manag. Sci. 1, 94–104.
  27. Lutkevich, B., 2024. What is Generative Design? Ultimate Guide | Definition from TechTarget [WWW Document]. WhatIs. URL https://www.techtarget.com/whatis/definition/generative-design (accessed 3.25.25).
  28. Luu, R.K., Arevalo, S., Lu, W., Ni, B., Yang, Z., Shen, S.C., Berkovich, J., Hsu, Y.-C., Zan, S., Buehler, M.J., 2024. Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design. MIT Explor. Gener. AI. https://doi.org/10.21428/e4baedd9.33bd7449
  29. Madanchian, M., 2024. Generative AI for Consumer Behavior Prediction: Techniques and Applications. Sustainability 16, 9963. https://doi.org/10.3390/su16229963
  30. Mbah, G., 2024. The Role of Artificial Intelligence in Shaping Future Intellectual Property Law and Policy: Regulatory Challenges and Ethical Considerations 5023–5037. https://doi.org/10.55248/gengpi.5.1024.3123
  31. Midjourney [WWW Document], 2025. Midjourney. URL https://www.midjourney.com/website (accessed 7.4.25).
  32. Monser, M., Fadel, E., 2023. A modern vision in the applications of artificial intelligence in the field of visual arts. Int. J. Multidiscip. Stud. Art Technol. 6, 73–104. https://doi.org/10.21608/ijmsat.2024.274900.1021
  33. Ok, E., Emmanuel, J., 2025. Ethical Considerations of AI-Generated Art in the Graphic Design Industry.
  34. Özsoy, H.Ö., 2025. AI-Driven Tools for Advancing the Industrial Design Process – A Literature Review. Gazi Univ. J. Sci. Part B Art Humanit. Des. Plan. 13, 77–96.
  35. Özsoy, H.Ö., 2020. Evaluation of Competitiveness in Product Design by Using the Analytic Hierarchy Process. İstanbul Ticaret Üniversitesi Sos. Bilim. Derg. 19, 655–677.
  36. Özsoy, H.Ö., 2009. Endüstri ürünleri tasarımında eğretilemeli anlatımlar ve tasarım yaklaşımı olarak yöntemli kullanımı (doctoralThesis). Mimar Sinan Güzel Sanatlar Üniversitesi Fen Bilimleri Enstitüsü.
  37. Patel, J., Tale, K., Bidwe, R.V., Mishra, S., Deshmukh, G., Shinde, S., 2024. PromptArt: AI-Powered Image Generation, in: 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). Presented at the 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1–6. https://doi.org/10.1109/ICCUBEA61740.2024.10775159
  38. Peavey, E.K., Zoss, J., Watkins, N., 2012. Simulation and Mock-Up Research Methods to Enhance Design Decision Making. HERD 5, 133–144. https://doi.org/10.1177/193758671200500313
  39. Quan, H., Li, S., Zeng, C., Wei, H., Hu, J., 2023. Big Data and AI-Driven Product Design: A Survey. Appl. Sci. 13, 9433. https://doi.org/10.3390/app13169433
  40. Regenwetter, L., Nobari, A.H., Ahmed, F., 2022. Deep Generative Models in Engineering Design: A Review. J. Mech. Des. 144. https://doi.org/10.1115/1.4053859
  41. Saadi, J.I., 2024. Generative Design Tools: Implications on Design Process, Designer Behavior, and Design Outcomes (Thesis). Massachusetts Institute of Technology.
  42. SPSS 30 [WWW Document], 2024. URL https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-30 (accessed 7.4.25).
  43. Stahle, L., Wold, S., 1989. Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6, 259–272. https://doi.org/10.1016/0169-7439(89)80095-4
  44. Subramonyam, H., Thakkar, D., Ku, A., Dieber, J., Sinha, A.K., 2025. Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams, in: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25. Association for Computing Machinery, New York, NY, USA, pp. 1–22. https://doi.org/10.1145/3706598.3713166
  45. Tammisto, E., 2025. Usage of artificial intelligence in industrial design processes. Tekoälyn hyödyntäminen osana teollisen muotoilun prosesseja.
  46. Thongmeensuk, S., 2024. Rethinking copyright exceptions in the era of generative AI: Balancing innovation and intellectual property protection. J. World Intellect. Prop. 27, 278–295. https://doi.org/10.1111/jwip.12301
  47. Tsang, Y.P., Lee, C.K.M., 2022. Artificial intelligence in industrial design: A semi-automated literature survey. Eng. Appl. Artif. Intell. 112, 104884. https://doi.org/10.1016/j.engappai.2022.104884
  48. Villalba, M., Palomar, M., 2024. A review of AI application trends in industrial design.
  49. Vizcom AI [WWW Document], 2025. Online Artif. Intell. Vis. Serv. URL https://www.vizcom.ai/ (accessed 2.22.25).
  50. Zhang, Z., Yin, H., 2024. Research on design forms based on an artificial intelligence collaboration model. Cogent Eng. 11, 2–18.
  51. Zimmerling, C., Poppe, C., Kärger, L., 2019. Virtual Product Development Using Simulation Methods and AI. Lightweight Des. Worldw. 12, 12–19. https://doi.org/10.1007/s41777-019-0064-x
  52. Boggs, C., 2010. Mock-ups in design: the implications of utlizing [sic] a mock-up review process in professional practice. FIU Electron. Theses Diss. https://doi.org/10.25148/etd.FI14051179
  53. Burlin, C., 2023. Explainability to enhance creativity: A human-centered approach to prompt engineering and task allocation in text-to-image models for design purposes.
  54. Vizcom AI [WWW Document], 2025. Online Artif. Intell. Vis. Serv. URL https://www.vizcom.ai/ (accessed 2.22.25).