BetaDAPR: An AI-based Expert Support System for Art Therapists with Qualitative and Quantitative Assistance
Migyeong Yang, Chaehee Park, Hyunseon Won, Taeeun Kim, Hayeon Song, Jinyoung Han
Abstract
Sketch-based drawing assessments are valuable for understanding cognitive and psychological states but rely on therapists’ expertise, making them labor-intensive. While a few automated methods have been proposed, their capability is limited to supporting quantitative analysis, which falls short of qualitative needs in art therapy. To overcome this limitation, we introduce BetaDAPR , an expert support system offering both qualitative and quantitative assistance for large-scale drawing assessments. To investigate how the presence or absence of our system’s qualitative assistance is perceived depending on the art therapist’s level of expertise, we conducted a 2 × 2 factorial design experiment. Results showed that qualitative support improved therapists’ intention to use the system and increased trust, especially for senior therapists, enhancing acceptance of its physical presence. Interviews revealed that AI assistance is differently perceived by the therapists’ experience level, affecting system evaluations. We believe our findings shed light on developing AI-based expert support systems for art therapy.
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