Human–AI Collaborative Art Therapy for Stress Reduction and Emotional Regulation
Keywords:
human–AI collaboration; art therapy; emotion regulation; digital mental health; responsible artificial intelligence; socio-technical systems; affective computing; clinical governanceAbstract
Art therapy has historically relied on human attunement, creative expression, and embodied interaction to support stress reduction and emotional regulation. The increasing integration of artificial intelligence into expressive therapeutic settings introduces new possibilities for adaptive multimodal co-creation, but it also raises deep structural questions about system architecture, clinical responsibility, data governance, and equitable access. This paper examines human–AI collaborative art therapy as a socio-technical intervention rather than a narrowly technical application. It develops a system-level analysis of the architectural choices, interaction mechanisms, infrastructure requirements, and institutional arrangements that shape whether such systems can be safe, robust, and sustainable outside controlled research environments. The discussion considers layered sensing and generative components, explainability and bias auditing, privacy-preserving data infrastructures, clinical oversight, and policy mechanisms for deployment. By treating art therapy as a distributed coordination process among human therapists, clients, creative interfaces, and learning systems, the paper identifies structural trade-offs between personalization and clinical standardization, between expressive openness and risk management, and between innovation speed and regulatory accountability. The analysis draws on affective computing, human-centered artificial intelligence, digital mental health, and responsible AI scholarship. It argues that successful human–AI art therapy must be designed as a governed, interpretable, and sustainable ecosystem in which machine agency remains subordinate to therapeutic judgment and the relational core of art therapy. The paper concludes with forward-looking implications for infrastructure investment, reimbursement policy, professional training, and cross-institutional governance.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
