AlphaDAPR: An AI-based Explainable Expert Support System for Art Therapy
- Authors
- Kim, J.; Kang, J.; Kim, T.; Song, H.; Han, J.
- Issue Date
- Mar-2023
- Publisher
- Association for Computing Machinery
- Keywords
- AI; Art therapy; Draw-A-Person-in-the-Rain; Drawing assessments; Expert support system; Explainable AI
- Citation
- International Conference on Intelligent User Interfaces, Proceedings IUI, pp 19 - 31
- Pages
- 13
- Indexed
- SCOPUS
- Journal Title
- International Conference on Intelligent User Interfaces, Proceedings IUI
- Start Page
- 19
- End Page
- 31
- URI
- https://scholarx.skku.edu/handle/2021.sw.skku/104282
- DOI
- 10.1145/3581641.3584087
- ISSN
- 0000-0000
- Abstract
- Sketch-based drawing assessments in art therapy are widely used to understand individuals' cognitive and psychological states, such as cognitive impairment or mental disorders. Along with self-report measures based on a questionnaire, psychological drawing assessments can augment information about an individual psychological state. However, the interpretation of the drawing assessments requires much time and effort, especially in a large-scale group such as schools or companies, and depends on the experience of the art therapists. To address this issue, we propose an AI-based expert support system, AlphaDAPR, to support art therapists and psychologists in conducting a large-scale automatic drawing assessment. Our survey results with 64 art therapists showed that 64.06% of the participants indicated a willingness to use the proposed system. The results of structural equation modeling highlighted the importance of explainable AI embedded in the interface design to affect perceived usefulness, trust, satisfaction, and intention to use eventually. The interview results revealed that most of the art therapists show high levels of intention to use the proposed system while expressing some concerns about AI's possible limitations and threats as well. Discussion and implications are provided, stressing the importance of clear communication about the collaborative role of AI and users. © 2023 ACM.
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Collections - Computing and Informatics > Convergence > 1. Journal Articles

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