PracticeDAPR: An AI-based Education-Supported System for Art Therapy
Migyeong Yang, Chaehee Park, Jiwon Kang, Jiwon Kim, Taeeun Kim, Hayeon Song, Jinyoung Han
Abstract
In this paper, we propose PracticeDAPR, an AI-based education-supported system for beginners in DAPR assessment practice. As professional identity is considered a pivotal goal in art therapy education, it is important to help beginners not to experience difficulties in professional identity development. Therefore, we designed the proposed system to provide the following three factors, which are closely associated with the professional identity formation of beginners in art therapy: (i) performance improvement, (ii) anxiety reduction, and (iii) self-efficacy enhancement. To this end, we adopt online peer-to-peer learning as the foundational learning approach. In addition, by introducing AI as a mentor, we let users not only interact with their peers but also experience AI assistance. The user study targeting graduate students in art therapy was conducted with both quantitative and qualitative methods. In general, users reported positive experiences with PracticeDAPR. The results of the structural equation model analysis showed that perceived usefulness is an important contributor to the three factors, highlighting the effectiveness of online peer-to-peer learning with the AI mentor. Furthermore, by deriving the results that intention to use can be promoted by performance improvement, it is demonstrated that PracticeDAPR can consistently help the development of the professional identity, which is not easily established in a short period of time. Discussion and implications are provided in relation to using AI and online peer-to-peer learning to support current art therapy education.
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