From Static To Sentient: Designing Emotionally Responsive Interfaces Using Affective Computing For UX Enhancement

Authors

  • Dedy Prasetya Dedy-University of 17 Agustus 1945 Surabaya
  • Ika Inayati Budi Utami Ika-University of 17 Agustus 1945 Surabaya
  • Silvi Pertiwi Silvi-University of 17 Agustus 1945 Surabaya

DOI:

https://doi.org/10.51903/ijgd.v3i1.2811

Keywords:

Artificial Intelligence, User Experience, Affective Computing, Human-Centered Design, Creative Industries

Abstract

Abstract

This study explores the integration of artificial intelligence (AI), particularly generative and affective computing, into user experience (UX) and creative industry workflows. It investigates how recent advancements in multimodal AI, user interface (UI) design, and emotion recognition can enhance personalization, user satisfaction, and design efficiency. Drawing from cross-disciplinary literature, the paper highlights the transformative potential of tools such as DALL·E, Midjourney, and Adobe Firefly in supporting ideation and prototyping, while also addressing concerns about emotional authenticity, ethical transparency, and cultural sensitivity. Findings suggest that AI-driven UX innovations must be grounded in human-centered design to retain user agency and trust, especially in emotionally sensitive contexts. The study emphasizes the role of affective computing in enabling adaptive digital environments through real-time emotion recognition. However, limitations related to the generalizability of findings, lack of empirical testing, and rapid technological evolution are acknowledged. Future research directions include empirical validation of AI-UX frameworks, cross-cultural testing, and interdisciplinary collaboration to ensure ethical, inclusive, and emotionally intelligent design systems. Overall, the study contributes to a growing discourse on the responsible integration of AI in UX, proposing that technology should act as a co-creative partner rather than a replacement for human creativity and empathy.

References

Abdullah, S. M. S. A., Ameen, S. Y. A., M. Sadeeq, M. A., & Zeebaree, S. (2021). Multimodal Emotion Recognition using Deep Learning. Journal of Applied Science and Technology Trends, 2(01), 73–79. https://doi.org/10.38094/jastt20291

Al-Hunaiyyan, A., Alhajri, R., Alghannam, B., & Al-Shaher, A. (2021). Student Information System: Investigating User Experience (UX). In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 12, Issue 2). www.ijacsa.thesai.org

Amin, M. M., Cambria, E., & Schuller, B. W. (2023). Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT. http://arxiv.org/abs/2303.03186

Anantrasirichai, N., & Bull, D. (2022). Artificial Intelligence in the Creative Industries: A Review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7

Balasubramanian, M. (2024). Rapid Design Prototyping using Generative Artificial Intelligence: A Case Study Comparing DALL-E, Midjourney and Firefly. https://docs.midjourney.com/

Bisset Delgado, C. (2022). User Experience (UX) in Metaverse: Realities and Challenges. Metaverse Basic and Applied Research, 1, 9. https://doi.org/10.56294/mr20229

Borre, J. R., Romero, G. C., Gutierrez, J. M., & Ramirez, J. (2023). Discussion of the Aspects of the Cultural and Creative Industries that Impact on Sustainable Development: A Systematic Review. Procedia Computer Science, 224, 532–537. https://doi.org/10.1016/j.procs.2023.09.077

Campbell, H. A., & Tsuria, R. (Eds.). (2022). Digital Religion: Understanding Religious Practice in Digital Media (2nd ed.). Routledge. https://doi.org/10.4324/9780429295683

Deng, J., & Ren, F. (2023). A Survey of Textual Emotion Recognition and Its Challenges. IEEE Transactions on Affective Computing, 14(1), 49–67. https://doi.org/10.1109/taffc.2021.3053275

Gervasi, R., Barravecchia, F., Mastrogiacomo, L., & Franceschini, F. (2023). Applications of Affective Computing in Human-Robot Interaction: State-of-art and Challenges for Manufacturing. In Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture (Vol. 237, Issues 6–7, pp. 815–832). SAGE Publications Ltd. https://doi.org/10.1177/09544054221121888

Gunawan, R., Anthony, G., Vendly, & Anggreainy, M. S. (2021). The Effect of Design User Interface (UI) E-Commerce on User Experience (UX). Proceedings of 2021 6th International Conference on New Media Studies, CONMEDIA 2021, 95–98. https://doi.org/10.1109/conmedia53104.2021.9617199

Hammer, M. (2020). Identifying Antecedents to Learning Effectively with Digital Media: A Student-Centered Approach (Doctoral dissertation, Eberhard Karls Universität Tübingen). https://doi.org/10.15496/publikation-51795

Hu, G., Xin, Y., Lyu, W., Huang, H., Sun, C., Zhu, Z., Gui, L., Cai, R., Cambria, E., & Seifi, H. (2024). Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective. http://arxiv.org/abs/2409.07388

Huang, J. (2024). The Art of AI: A Human-Centered AI (HCAI) User Study of Integrating Image-Generative Tools in Visual Art Workflows: The Case of Adobe Firefly (Master's thesis, KTH Royal Institute of Technology). https://doi.org/10.16993/kvbq

Jin, J., Yang, M., Hu, H., Guo, X., Luo, J., & Liu, Y. (2025). Empowering Design Innovation using AI-Generated Content. Journal of Engineering Design, 36(1), 1–18. https://doi.org/10.1080/09544828.2024.2401751

Kazemitabaar, M., Huang, O., Suh, S., Henley, A. Z., & Grossman, T. (2024). Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning. http://arxiv.org/abs/2410.08922

Koch, F., Hoellen, M., Konrad, E. D., & Kock, A. (2023). Innovation in the Creative Industries: Linking the Founder’s Creative and Business Orientation to Innovation Outcomes. Creativity and Innovation Management, 32(2), 281–297. https://doi.org/10.1111/caim.12554

Lee, H. K. (2022). Rethinking Creativity: Creative Industries, AI and Everyday Creativity. Media, Culture and Society, 44(3), 601–612. https://doi.org/10.1177/01634437221077009

Liu, Y., Xu, Y., & Song, R. (2024). Transforming User Experience (UX) through Artificial Intelligence (AI) in Interactive Media Design. World Journal of Innovation and Modern Technology, 7(5), 30–39. https://doi.org/10.53469/wjimt.2024.07(05).03

MacDonald, C. M., Sosebee, J., & Srp, A. (2022). A Framework for Assessing Organizational User Experience (UX) Capacity. International Journal of Human-Computer Interaction, 38(11), 1064–1080. https://doi.org/10.1080/10447318.2021.1979811

Prihatmoko, D., Falahah, & Dwiastanti, A. (2024). The Role of Visual Communication Strategies in Optimizing Social Media Engagement. International Journal of Graphic Design, 4(1), 45–56. https://doi.org/10.51903/ijgd.v2i2.2113

Saranya, R., Wibowo, A., & Lestari, M. (2025). Cultural Influences on User Interface Personalization in Cross-Platform Design. International Journal of Graphic Design, 5(1), 12–24. https://doi.org/10.51903/ijgd.v3i1.2752

Saleem Abdullah, S. M., Ameen, S. Y., Sadeeq, M. A. M., & Zeebaree, S. R. M. (2021). Multimodal Emotion Recognition using Deep Learning. Journal of Applied Science and Technology Trends, 2(1), 73–79. https://doi.org/10.38094/jastt20291

Silva, S. R., Marques, C. S. E., & Galvão, A. R. (2024). Where Is the Rural Creative Class? A Systematic Literature Review About Creative Industries in Low-Density Areas. Journal of the Knowledge Economy, 15(2), 6026–6056. https://doi.org/10.1007/s13132-023-01341-6

Yunianto, I., & Wahyudi, W. (2024). Designing User Experience For a Mobile Application for Agricultural Product Marketing using the Human-Centered Design Method. International Journal of Graphic Design, 2(2), 207–221. https://doi.org/10.51903/ijgd.v2i2.2123

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Published

2025-05-30

How to Cite

From Static To Sentient: Designing Emotionally Responsive Interfaces Using Affective Computing For UX Enhancement. (2025). International Journal of Graphic Design, 3(1), 120-136. https://doi.org/10.51903/ijgd.v3i1.2811