Developing an AI-based Explainable Expert Support System for Art Therapy

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairments or mental disorders. Along with self-reported measures based on questionnaires, psychological drawing assessments can augment information regarding an individual’s psychological state. Interpreting drawing assessments demands significant time and effort, particularly for large groups such as schools or companies, and relies on the expertise of art therapists. To address this issue, we propose an artificial intelligence (AI)-based expert support system called AlphaDAPR to support art therapists and psychologists in conducting large-scale automatic drawing assessments. In Study 1, we first investigated user experience in AlphaDAPR . Through surveys involving 64 art therapists, we observed a substantial willingness (64.06% of participants) in using the proposed system. Structural equation modeling highlighted the pivotal role of explainable AI in the interface design, affecting perceived usefulness, trust, satisfaction, and intention to use. However, our interviews unveiled a nuanced perspective: while many art therapists showed a strong inclination to use the proposed system, they also voiced concerns about potential AI limitations and risks. Since most concerns arose from insufficient trust, which was the focal point of our attention, we conducted Study 2 with the aim of enhancing trust. Study 2 delved deeper into the necessity of clear communication regarding the division of roles between AI and users for elevating trust. Through experimentation with another 26 art therapists, we demonstrated that clear communication enhances users’ trust in our system. Our work not only highlights the potential of AlphaDAPR to streamline drawing assessments but also underscores broader implications for human-AI collaboration in psychological domains. By addressing concerns and optimizing communication, we pave the way for a symbiotic relationship between AI and human expertise, ultimately enhancing the efficacy and accessibility of psychological assessment tools.

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Publication Details

Journal
ACM Transactions on Interactive Intelligent Systems
Published
2024-08-30
DOI
https://doi.org/10.1145/3689649
Citations
5
Primary Topic
Art Therapy and Mental Health
Type
article
Field-Weighted Citation Impact
9.78
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article

Developing an AI-based Explainable Expert Support System for Art Therapy

Jiwon Kim, Migyeong Yang, Jinyoung Han, Hayeon Song et al.
5 citations
ACM Transactions on Interactive Intelligent Systems
Art Therapy and Mental Health
9.78
article

Developing an AI-based Explainable Expert Support System for Art Therapy

Jiwon Kim, Migyeong Yang, Jinyoung Han, Hayeon Song, Taeeun Kim, Jiwon Kang, Chaehee Park
article en
5 citations

Abstract

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairments or mental disorders. Along with self-reported measures based on questionnaires, psychological drawing assessments can augment information regarding an individual’s psychological state. Interpreting drawing assessments demands significant time and effort, particularly for large groups such as schools or companies, and relies on the expertise of art therapists. To address this issue, we propose an artificial intelligence (AI)-based expert support system called AlphaDAPR to support art therapists and psychologists in conducting large-scale automatic drawing assessments. In Study 1, we first investigated user experience in AlphaDAPR . Through surveys involving 64 art therapists, we observed a substantial willingness (64.06% of participants) in using the proposed system. Structural equation modeling highlighted the pivotal role of explainable AI in the interface design, affecting perceived usefulness, trust, satisfaction, and intention to use. However, our interviews unveiled a nuanced perspective: while many art therapists showed a strong inclination to use the proposed system, they also voiced concerns about potential AI limitations and risks. Since most concerns arose from insufficient trust, which was the focal point of our attention, we conducted Study 2 with the aim of enhancing trust. Study 2 delved deeper into the necessity of clear communication regarding the division of roles between AI and users for elevating trust. Through experimentation with another 26 art therapists, we demonstrated that clear communication enhances users’ trust in our system. Our work not only highlights the potential of AlphaDAPR to streamline drawing assessments but also underscores broader implications for human-AI collaboration in psychological domains. By addressing concerns and optimizing communication, we pave the way for a symbiotic relationship between AI and human expertise, ultimately enhancing the efficacy and accessibility of psychological assessment tools.

ACM Transactions on Interactive Intelligent SystemsVol. 14(4)
CHA University (KR), Sungkyunkwan University (KR)
Openalex Percentile: Top 2%
Art Therapy and Mental Health
9.78
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