Interactive Generative Art for Enhancing Emotional Expression in Digital Mental Health

Authors

  • Jerome M. Dawson Department of Computer Science, University of North Texas, Denton, TX, USA.
  • Gerald Koskinen Department of Computer Science, University of Houston, Houston, TX, USA.
  • Qianbo Sun School of Computing, Clemson University, Clemson, SC, USA.

Keywords:

interactive generative art; digital mental health; affective computing; human-AI co-creation; socio-technical systems; governance

Abstract

Interactive generative art occupies a promising yet underexamined position between expressive arts interventions and computational mental health support. This paper develops a system-level account of how interactive generative art can enhance emotional expression in digital mental health. Rather than treating generative models as isolated technical artifacts, the analysis focuses on architectural composition, interaction infrastructure, deployment pressures, governance constraints, robustness requirements, fairness challenges, and long-term sustainability. The discussion integrates affective computing, human-computer interaction, generative modeling, and clinical implementation research to identify structural trade-offs that shape expressive benefits and risks. A central argument is that emotional expression is not produced solely by an image or sound generator; it emerges from the orchestration of perception, interpretation, feedback loops, clinical framing, and sociotechnical trust. Interactive generative art can externalize difficult emotional states, scaffold reflective awareness, and support therapeutic dialogue, but only when systems are designed with attention to latency, interpretability, safety, and inclusive representation. The paper further examines regulatory tensions around automated mental health tools, the need for robust failure management, and conditions for sustainable deployment across diverse care settings. By reframing generative art as a system-level intervention rather than a feature-level enhancement, the paper offers a structural lens for researchers, clinicians, and policy actors seeking to align computational creativity with the ethical demands of mental health care.

References

1. Torous, J., Myrick, K. J., Rauseo-Ricupero, N., & Firth, J. (2020). Digital mental health and COVID-19: Using technology today to accelerate the curve on access and quality tomorrow. JMIR Mental Health, 7(3), e18848. https://doi.org/10.2196/18848

2. Picard, R. W. (1997). Affective computing. MIT Press.

3. Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47. https://doi.org/10.1146/annurev-clinpsy-032816-044949

4. Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1125-1134). IEEE.

5. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622

6. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125.

7. Sonderegger, A., & Sauer, J. (2010). The influence of design aesthetics in usability testing: Effects on user performance and perceived usability. Applied Ergonomics, 41(3), 403-410. https://doi.org/10.1016/j.apergo.2009.09.002

8. Norman, D. A. (2013). The design of everyday things (Revised ed.). Basic Books.

9. Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2016). Mental health smartphone apps: Review and evidence-based recommendations for future developments. JMIR Mental Health, 3(1), e7. https://doi.org/10.2196/mental.4984

10. Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. https://doi.org/10.1145/365153.365168

11. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13). ACM. https://doi.org/10.1145/3290605.3300233

12. Wang, Z., Ma, L., Jin, Y., Feng, Y., Pan, X., Ji, S., & Zhang, K. (2025, August). AI-assisted human-pet artistic musical co-creation for wellness therapy. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 10216-10224).

13. Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76-99. https://doi.org/10.1093/idpl/ipx005

14. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

15. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77-91.

16. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35. https://doi.org/10.1145/3457607

17. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). ACM. https://doi.org/10.1145/3442188.3445922

18. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

19. Riva, G., Baños, R. M., Botella, C., Mantovani, F., & Gaggioli, A. (2016). Transforming experience: The potential of augmented reality and virtual reality for enhancing personal and clinical change. Frontiers in Psychiatry, 7, 164. https://doi.org/10.3389/fpsyt.2016.00164

20. Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022). From promise to practice: Towards the realisation of AI-informed mental health care. The Lancet Digital Health, 4(11), e829-e840. https://doi.org/10.1016/S2589-7500(22)00153-4

21. Calvo, R. A., & Peters, D. (2014). Positive computing: Technology for wellbeing and human potential. MIT Press.

22. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining gamification. In Proceedings of the 15th International Academic MindTrek Conference (pp. 9-15). ACM. https://doi.org/10.1145/2181037.2181040

Downloads

Published

2026-08-26

Issue

Vol. 1 No. 2 (2026): Journal of Engineering Systems and Digital Innovation

Section

Articles

License

Copyright (c) 2026 Journal of Engineering Systems and Digital Innovation

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.