Abstract
Intense competition across various aspects of contemporary society burdens individuals with tremendous pressure, while extreme public crises like natural disasters and pandemics further heavier residents’ mental problems. Artificial Intelligence (AI) painting combines technology and art, lowering the barriers to creation and offering a promising avenue for alleviating feelings of isolation, fostering self-discovery, and improving mental health. This research organized several AI painting healing workshops, engaging 136 volunteers, and randomized them into groups using computer-generated sequences. Researchers conducted a Pre-Post Descriptive Analysis and chose the Profile of the Mood States 2nd Edition (POMS 2) to measure participants’ psychological states before and after their AI painting workshop experience, thereby calculating the mean (μ) and standard deviation (σ) for analysis. The results revealed a decrease in values associated with negative emotions among participants after the workshop, particularly in Confusion-Bewilderment (CB) scores (from 15.56 to 12.03) and Tension-Anxiety (TA) scores (from 14.92 to 12.48). By contrast, Scores related to positive emotions, like Vigor-Activity (VA), showed an increased trend (from 5.92 to 9.47). The Pre-Post Descriptive Analysis demonstrated the healing potential of AI painting workshops, offering an innovative interdisciplinary approach that combines technology, art therapy, and mental health strategies. As AI technology continues to intertwine with society, AI could not only enhance economic productivity but also serve as a tool for human connection, creativity, and psychological well-being.
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References
Cénat, J.M., et al.: Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: a systematic review and meta-analysis. Psychiatry Res. 295, 113599 (2021)
Crowder, J.A., Carbone, J., Friess, S. (eds.): Artificial Psychology: Psychological Modeling and Testing of AI Systems. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17081-3
Floridi, L.: Artificial intelligence, deepfakes and a future of ectypes. In: Ethics, Governance, and Policies in Artificial Intelligence, pp. 307–312 (2021)
Guerreiro, R.I.C.: Using artificial intelligence to create paintings: How type of artist impacts WTP through emotional intelligence and perceived quality [PhD Thesis] (2022). https://repositorio.ucp.pt/handle/10400.14/38837
Lyu, Y., Wang, X., Lin, R., Wu, J.: Communication in human–AI co-creation: perceptual analysis of paintings generated by text-to-image system. Appl. Sci. 12(22), 11312 (2022)
Mazzone, M., Elgammal, A.: Art, creativity, and the potential of artificial intelligence. Arts 8(1), 26 (2019)
Ragot, M., Martin, N., Cojean, S.: Ai-generated vs. Human artworks. A perception bias towards artificial intelligence? In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–10 (2020)
Shacham, S.: A shortened version of the profile of mood states. J. Pers. Assess. 47(3), 305–306 (1983). https://doi.org/10.1207/s15327752jpa4703_14
Turner, A.I., et al.: Psychological stress reactivity and future health and disease outcomes: a systematic review of prospective evidence. Psychoneuroendocrinology 114, 104599 (2020)
Wang, Y., Sun, Y.: The relevance of emotional AI-generated painting to the painting subject and main colors. In: Mori, H., Asahi, Y., Coman, A., Vasilache, S., Rauterberg, M. (eds.) HCI International 2023 – Late Breaking Papers, vol. 14056, pp. 390–399. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-48044-7_28
Yalçın, Ö.N., Abukhodair, N., DiPaola, S.: Empathic ai painter: a computational creativity system with embodied conversational interaction. In: NeurIPS 2019 Competition and Demonstration Track, pp. 131–141 (2020). https://proceedings.mlr.press/v123/yalcin20a.html
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Gao, T., Yang, M., Ning, J., Qiao, Y., Zhou, H. (2024). Overcome Psychological Alienation Through Artificial Intelligence Painting Healing Workshops. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2024. Lecture Notes in Computer Science, vol 14686. Springer, Cham. https://doi.org/10.1007/978-3-031-60428-7_3
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DOI: https://doi.org/10.1007/978-3-031-60428-7_3
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