SavedAdd your journal

Journals / Bilişim Teknolojileri Dergisi / 2025 / Cilt: 18 Sayı: 4

The Effect of Design Students' Command Interactions in Artificial Intelligence Applications on User Experience: An Exploratory Study with Psychophysiological Data Harvesting

Tasarım Öğrencilerinin Yapay Zekâ Uygulamalarındaki Komut Etkileşimlerinin Kullanıcı Deneyimine Etkisi: Psikofizyolojik Veri Hasadı ile Keşifsel Bir Çalışma

Barış Atiker(AYDIN ADNAN MENDERES ÜNİVERSİTESİ, İLETİŞİM FAKÜLTESİ)0000-0002-4622-7409
Behiç Alp Aytekin(Adnan Menderes Üniversitesi İletişim Fakültesi)0000-0001-9121-6445
Neslihan Erdem(AYDIN ADNAN MENDERES ÜNİVERSİTESİ, İLETİŞİM FAKÜLTESİ)0000-0002-2506-3564
Ezgi Şen Atiker0000-0003-2886-7098
Pages
305–317
Publication Date
2025-10-31
🏛️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 study found that generative AI can greatly improve design processes, but the interfaces need to be better to help users control and explore creatively without feeling stressed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In recent years, people have been talking a lot about how generative AI affects design.
📊
Key Findings & Data:It studied tools like Ideogram.ai and Krea.ai with 15 visual communication design students at a university in Turkey.
💡
Conclusion & Impact:The study found that generative AI can greatly improve design processes, but the interfaces need to be better to help users control and explore creatively without feeling stressed.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Üretken Yapay Zeka · Görsel İletişim Tasarımı · Yapay Zeka ile Yaratıcılık

Abstract

In recent years, people have been talking a lot about how generative AI affects design. These technologies change how we usually do design and bring new chances and problems. This study looked at how generative AI affects design, focusing on creativity and user control. It studied tools like Ideogram.ai and Krea.ai with 15 visual communication design students at a university in Turkey. The study used a technique called Psychophysiological Data Harvesting (PDH) to analyze student experiences and creative results. It compared text-to-image and sketch-to-image design processes using eye tracking, electrodermal activity, and post-test questions. The study found that generative AI can greatly improve design processes, but the interfaces need to be better to help users control and explore creatively without feeling stressed.

Özet

Son yıllarda üretken yapay zekânın tasarım üzerindeki etkisi sıkça tartışılmaktadır. Bu teknolojiler, geleneksel tasarım süreçlerini dönüştürmekte ve beraberinde hem yeni olanaklar hem de çeşitli sorunlar getirmektedir. Bu çalışma, üretken yapay zekânın tasarım üzerindeki etkisini yaratıcılık ve kullanıcı kontrolü odağında incelemiştir. Türkiye’deki bir üniversitede öğrenim gören 15 Görsel İletişim Tasarımı öğrencisiyle yürütülen araştırmada, Ideogram.ai ve Krea.ai gibi araçlar kullanılmıştır. Psikofizyolojik Veri Hasadı (PVH) tekniği ile öğrencilerin deneyimleri ve ortaya çıkan yaratıcı çıktılar analiz edilmiştir. Metinden görsele ve eskizden görsele tasarım süreçleri, göz izleme, elektrodermal aktivite ölçümü ve son test soruları ile karşılaştırılmıştır. Bulgular, üretken yapay zekânın tasarım sürecini önemli ölçüde geliştirebileceğini, ancak kullanıcı arayüzlerinin yaratıcı keşfi destekleyecek şekilde geliştirilmesi gerektiğini göstermektedir.

🔖

Cite This Article

Atiker, B., Aytekin, B. A., Erdem, N., Atiker, E. Ş. (2025). The Effect of Design Students' Command Interactions in Artificial Intelligence Applications on User Experience: An Exploratory Study with Psychophysiological Data Harvesting. Bilişim Teknolojileri Dergisi, Cilt: 18 Sayı: 4, 305–317. https://doi.org/10.17671/gazibtd.1633452

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (53)

  1. K. Friedman, “Models of design: Envisioning a future design education,” Visible Language, vol. 46, no. 1-2, pp. 132–133, 2012.
  2. M. Meyer and D. Norman, “Changing Design Education for the 21st Century,” She Ji: The Journal of Design, Economics, and Innovation, vol. 6, pp. 13-49, 2020. Available at: https://doi.org/10.1016/j.sheji.2019.12.002.
  3. Y. Lai, H. Chen, and C. Yang, “Exploring the Impact of Generative Artificial Intelligence on the Design Process: Opportunities, Challenges, and Insights,” Artificial Intelligence, Social Computing and Wearable Technologies, 2023. Available at: https://doi.org/10.54941/ahfe1004178.
  4. D. Tuckwell, “(Still) Educating design thinking,” Communication Design, vol. 5, no. 1–2, pp. 131–144, 2017. Available at: https://doi.org/10.1080/20557132.2017.1385257.
  5. M. McLain, “Towards a signature pedagogy for design and technology education: a literature review,” International Journal of Technology and Design Education, vol. 32, pp. 1629-1648, 2021. Available at: https://doi.org/10.1007/s10798-021-09667-5.
  6. M. Groborz and E. Nęcka, “Creativity and Cognitive Control: Explorations of Generation and Evaluation Skills,” Creativity Research Journal, vol. 15, pp. 183-197, 2003. Available at: https://doi.org/10.1080/10400419.2003.9651411.
  7. V. Bilgram and F. Laarmann, “Accelerating Innovation with Generative AI: AI-Augmented Digital Prototyping and Innovation Methods,” IEEE Engineering Management Review, vol. 51, pp. 18-25, 2023. Available at: https://doi.org/10.1109/EMR.2023.3272799.
  8. M. Garcia, “The Paradox of Artificial Creativity: Challenges and Opportunities of Generative AI Artistry,” Creativity Research Journal, 2024. Available at: https://doi.org/10.1080/10400419.2024.2354622.
  9. N. Rajcic, M. Rodriguez, and J. McCormack, “Towards a Diffractive Analysis of Prompt-Based Generative AI,” Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024. Available at: https://doi.org/10.1145/3613904.3641971.
  10. T. Nawar, “Generative Artificial Intelligence and Authorship Gaps,” American Philosophical Quarterly, 2024. Available at: https://doi.org/10.5406/21521123.61.4.05.
  11. L. Ruiz-Rojas, L. Salvador-Ullauri, and P. Acosta-Vargas, “Collaborative Working and Critical Thinking: Adoption of Generative Artificial Intelligence Tools in Higher Education,” Sustainability, 2024. Available at: https://doi.org/10.3390/su16135367.
  12. M. Verheijden and M. Funk, “Collaborative Diffusion: Boosting Designerly Co-Creation with Generative AI,” Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023. Available at: https://doi.org/10.1145/3544549.3585680.
  13. J. Guntupalli and K. Watanabe, “Integrating Generative AI for Enhanced Automation in System Design Processes,” 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-4, 2024. Available at: https://doi.org/10.1109/ETFA61755.2024.10710979.
  14. D. A. Schön, The Reflective Practitioner: How Professionals Think in Action, London: Ashgate Publishing, 1983.
  15. D. A. Schön, Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions, Jossey-Bass, 1987.
  16. X. O’Dea, “Generative AI: is it a paradigm shift for higher education?,” Studies in Higher Education, vol. 49, pp. 811-816, 2024. Available at: https://doi.org/10.1080/03075079.2024.2332944.
  17. M. Yan, Y. Li, Z. Zhang, and Q. Wang, “Generative AI as a learning partner: Enhancing students’ metacognitive and self-regulated learning in programming education,” Computers & Education, vol. 205, p. 105458, 2024, doi: 10.1016/j.compedu.2024.105458.
  18. A. Nguyen, S. Malik, and J. Thompson, “Rethinking academic writing with AI: Insights from doctoral students’ interactions with generative tools,” British Journal of Educational Technology, vol. 55, no. 1, pp. 84–102, 2024, doi: 10.1111/bjet.13455.
  19. R. Bartlett and J. D. Camba, “Educating Designers for AI: Recommendations for Teaching and Learning in the Age of Artificial Intelligence,” International Journal of Technology and Design Education, vol. 34, no. 1, pp. 271–291, 2024, doi: 10.1007/s10798-023-09768-9.
  20. S. Noy and W. Zhang, “Experimental evidence on the productivity effects of generative artificial intelligence,” Science, vol. 381, pp. 187-192, 2023. Available at: https://doi.org/10.1126/science.adh2586.
  21. M. Ghobakhloo, M. Iranmanesh, M. Fathi, A. Rejeb, B. Foroughi, and D. Nikbin, “Beyond Industry 4.0: A systematic review of Industry 5.0 technologies and implications for social, environmental and economic sustainability,” Asia-Pacific Journal of Business Administration, 2024, doi: 10.1108/APJBA-08-2023-0384.
  22. J. Kerr and N. Kelly, “Use of personas in co-designing learning experiences with teachers: An exploratory case study,” International Journal of Technology and Design Education, pp. 1-19, 2024. Available at: https://doi.org/10.1007/s10798-024-09900-x.
  23. R. A. Stebbins, Exploratory Research in the Social Sciences, SAGE Publications, 2001.
  24. R. K. Yin, “The abridged version of case study research,” in L. T. Bickman and J. R. Debra (Eds.), Handbook of Applied Social Research Methods, Thousand Oaks: Sage Publications, pp. 229–259, 1998.
  25. S. Aydın, K. Depboylu, and N. Erdem, “Biometric data harvesting: Proposals on remote biometric data harvesting and measurements in human behaviour scope,” in Current Debates on Social Sciences: Human Studies, pp. 107–113, Bilgin Kültür Sanat Yayınları, 2021.
  26. A. İcil Tuncer, B. A. Aytekin, M. S. Aydın, N. Erdem, K. Depboylu, T. A. Ulusoy, B. Üveyik, and T. Kızılhan, “Evaluating the relationship between logo and corporate reputation with psychophysiological data harvesting technique,” Business & Management Studies: An International Journal, vol. 11, no. 2, pp. 413–434, 2023. Available at: https://doi.org/10.15295/bmij.v11i2.2267.
  27. U. Garczarek-Bąk, “An Overview to Neuromarketing Research Methods,” Managing Economic Innovations–Methods and Instruments, vol. 54, 2019.
  28. B. Pierański, “Physiological Measurement As A (Controversial) Research Method,” Managing Economic Innovations–Methods and Instruments, vol. 38, 2019.
  29. J. Nielsen and K. Pernice, How to Conduct Eyetracking Studies, NNgroup, 2009.
  30. D. C. Richardson and D. Spivey, “Eye Tracking: Characteristics and Methods,”, 2004, DOI:10.1081/E-EBBE2-120013920.
  31. iMotions, Galvanic Skin Response: The Complete Pocket Guide, Denmark: iMotions, 2017.
  32. W. Boucsein, Electrodermal Activity, Springer, 2012. Available at: https://doi.org/10.1007/978-1-4614-1126-0.
  33. L. Margulieux, “Research design: Pre- and post-tests,” Lauren Margulieux, 2022. Available at: https://laurenmarg.com/2022/07/18/research-design-pre-and-post-tests/.
  34. J. Nielsen, “Why You Only Need to Test with 5 Users,” NNgroup, March 18, 2000. Available at: https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/.
  35. L. Faulkner, “Beyond the five-user assumption: Benefits of increased sample sizes in usability testing,” Behavior Research Methods, Instruments, & Computers, vol. 35, no. 3, pp. 379-383, 2003. Available at: https://doi.org/10.3758/BF03195514.
  36. N. Erdem and B. A. Aytekin, “A psychophysiological investigation of how visual perceptual skills and interaction experiences of visual communication design students change: Which eye tracking and electrodermal activity metrics to use?,” in Proceedings of the International Eastern Conference on Human-Computer Interaction (IECHCI2023), pp. 108–111, Erzurum, 2023.
  37. DeepL, DeepL Translator, 2025. Available at: https://www.deepl.com/tr/translator.
  38. Ideogram.ai, AI-powered image generation platform, 2025. Available at: https://ideogram.ai.
  39. Krea.ai, AI-powered creative tool for image generation, 2025. Available at: https://krea.ai.
  40. Adobe, Adobe Illustrator (Version 28.4) [Computer software], Adobe Inc., 2024. Available at: https://www.adobe.com/products/illustrator.html.
  41. R. Likert, “A technique for the measurement of attitudes,” Archives of Psychology, vol. 22, no. 140, pp. 1–55, 1932.
  42. S. S. Stevens, “On the theory of scales of measurement,” Science, vol. 103, no. 2684, pp. 677–680, 1946. Available at: https://doi.org/10.1126/science.103.2684.677.
  43. K. Holmqvist, M. Nyström, R. Andersson, R. Dewhurst, H. Jarodzka, and J. Van de Weijer, Eye tracking: A comprehensive guide to methods and measures, Oxford University Press, 2011.
  44. Y. Berzak, B. Katz, and R. Levy, “Assessing Language Proficiency from Eye Movements in Reading,” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1801–1811, 2018.
  45. A. Godfroid, Eye Tracking in Second Language Acquisition and Bilingualism: A Research Synthesis and Methodological Guide, Routledge, 2016.
  46. J. Sweller, P. Ayres, and S. Kalyuga, Cognitive load theory, Springer, 2019.
  47. J. L. Plass, R. Moreno, and R. Brünken, Eds., Cognitive load theory, Cambridge University Press, 2010. Available at: https://doi.org/10.1017/CBO9780511844744.
  48. J. Sweller, J. J. G. Van Merriënboer, and F. G. W. C. Paas, “Cognitive architecture and instructional design,” Educational Psychology Review, vol. 10, no. 3, pp. 251–296, 1998. Available at: https://doi.org/10.1023/A:1022193728205.
  49. J. Sweller and P. Chandler, “Why some material is difficult to learn,” Cognition and Instruction, vol. 12, no. 3, pp. 185–233, 1994. Available at: https://doi.org/10.1207/s1532690xci1203_1.
  50. J. Sweller, “Cognitive load theory, learning difficulty and instructional design,” Learning and Instruction, vol. 4, pp. 295–312, 1994. Available at: https://doi.org/10.1016/0959-4752(94)90003-5.
  51. J. Sweller and P. Chandler, “Evidence for cognitive load theory,” Cognition and Instruction, vol. 8, no. 4, pp. 351–372, 1991. Available at: https://doi.org/10.1207/s1532690xci0804_2.
  52. F. Tanhan, H. İ. Özok, and V. Tayiz, “Fear of Missing Out (FoMO): A current review,” Psikiyatride Güncel Yaklaşımlar, vol. 14, no. 1, pp. 74–85, 2022. Available at: https://doi.org/10.18863/pgy.942431.
  53. M. E. Dawson, A. M. Schell, and D. L. Filion, “The electrodermal system,” in Handbook of Psychophysiology, J. T. Cacioppo, L. G. Tassinary, and G. G. Berntson, Eds., 4th ed. Cambridge: Cambridge University Press, 2017, pp. 217–24.