IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i22p15830-d1277823.html
   My bibliography  Save this article

A Comprehensive Study of Emotional Responses in AI-Enhanced Interactive Installation Art

Author

Listed:
  • Xiaowei Chen

    (College of Creative Arts, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
    Academic Affairs Office, Zhejiang Shuren University, Hangzhou 310000, China)

  • Zainuddin Ibrahim

    (College of Creative Arts, Universiti Teknologi MARA, Shah Alam 40450, Malaysia)

Abstract

This study presents a comprehensive literature review on the convergence of affective computing, interactive installation art, multi-dimensional sensory stimulation, and artificial intelligence (AI) in measuring emotional responses, demonstrating the potential of artificial intelligence in emotion recognition as a tool for sustainable development. It addresses the problem of understanding emotional response and measurement in the context of interactive installation art under artificial intelligence (AI), emphasizing sustainability as a key factor. The study aims to fill the existing research gaps by examining three key aspects: sensory stimulation, multi-dimensional interactions, and engagement, which have been identified as significant contributors to profound emotional responses in interactive installation art. The proposed approach involves conducting a process analysis of emotional responses to interactive installation art, aiming to develop a conceptual framework that explores the variables influencing emotional responses. This study formulates hypotheses that make specific predictions about the relationships between sensory stimulation, multi-dimensional interactions, engagement, and emotional responses. By employing the ASSURE model combined with experimental design, the research methodology ensures a systematic and comprehensive study implementation. The implications of this project lie in advancing the understanding of emotional experiences in interactive installation art under AI, providing insights into the underlying mechanisms that drive these experiences, and their influence on individual well-being from a sustainable perspective. The contributions of this research include bridging the identified research gaps, refining theoretical frameworks, and guiding the design of more impactful and emotionally resonant interactive artworks with sustainability in mind. This research seeks not only to fill the existing gaps in understanding emotional experiences in interactive installation art, but also to guide the development of immersive and emotionally engaging installations, ultimately advancing the broader field of human–computer interaction, promoting individual well-being, and contribute to sustainable development.

Suggested Citation

  • Xiaowei Chen & Zainuddin Ibrahim, 2023. "A Comprehensive Study of Emotional Responses in AI-Enhanced Interactive Installation Art," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15830-:d:1277823
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/22/15830/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/22/15830/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Garima Verma & Hemraj Verma, 2020. "Hybrid-Deep Learning Model for Emotion Recognition Using Facial Expressions," The Review of Socionetwork Strategies, Springer, vol. 14(2), pages 171-180, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15830-:d:1277823. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.