Art Therapy (AT) is an established practice that facilitates emotional processing and recovery through creative expression. Recently, Visual Art Recommender Systems (VA RecSys) have emerged to support AT, demonstrating their potential by personalizing therapeutic artwork recommendations. Nonetheless, current VA RecSys rely on visual stimuli for user modeling, limiting their ability to capture the full spectrum of emotional responses during preference elicitation. Previous studies have shown that music stimuli elicit unique affective reflections, presenting an opportunity for cross-domain recommendation (CDR) to enhance personalization in AT. Since CDR has not yet been explored in this context, we propose a family of CDR methods for AT based on music-driven preference elicitation. A large-scale study with 200 users demonstrates the efficacy of music-driven preference elicitation, outperforming the classic visual-only elicitation approach. Our source code, data, and models are available at https://github.com/ArtAICare/Affect-aware-CDR

Post-intensive care syndrome (PICS) is a multifaceted condition that arises from prolonged stays in an intensive care unit (ICU). While preventing PICS among ICU patients is becoming increasingly important, interventions remain limited. Building on evidence supporting the effectiveness of art exposure in addressing the psychological aspects of PICS, we propose a novel art therapy solution through a collaborative Human-AI approach that enhances personalized therapeutic interventions using state-of-the-art Visual Art Recommendation Systems. We developed two Human-in-the-Loop (HITL) personalization methods and assessed their impact through a large-scale user study (N=150). Our findings demonstrate that this Human-AI collaboration not only enhances the personalization and effectiveness of art therapy but also supports therapists by streamlining their workload. While our study centres on PICS intervention, the results suggest that human-AI collaborative Art therapy could potentially benefit other areas where emotional support is critical, such as cases of anxiety and depression.

This paper explores the integration of generative AI into the field of art therapy. Leveraging proven text-to-image models, we introduce a novel technical design to complement art therapy. The resulting AI-based tools shall enable patients to refine and customize their creative work, opening up new avenues of expression and accessibility. Using three illustrative examples, we demonstrate potential outputs of our solution and evaluate them qualitatively. Furthermore, we discuss the current limitations and ethical considerations associated with this integration and provide an outlook into future research efforts. Our implementations are publicly available at https://github.com/BFH-AMI/sds24.

Storymaking, as an integrative form of expressive arts therapy, is an effective means to foster family communication. Yet, the integration of generative AI as expressive materials in therapeutic storymaking remains underexplored. And there is a lack of HCI implications on how to support families and therapists in this context. Addressing this, our study involved five weeks of storymaking sessions with seven families guided by a professional therapist. In these sessions, the families used both traditional art-making materials and image-based generative AI to create and evolve their family stories. Via the rich empirical data and commentaries from four expert therapists, we contextualize how families creatively melded AI and traditional expressive materials to externalize their ideas and feelings. Through the lens of Expressive Therapies Continuum (ETC), we characterize the therapeutic implications of AI as expressive materials. Desirable interaction qualities to support children, parents, and therapists are distilled for future HCI research.

While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices. In this work, we present an AI-based Brain-Computer Interface (BCI) in which humans and machines interact to express feelings artistically. This system and its production of images give opportunities to reflect on the complexities and range of human emotions and their expressions. In this discussion, we seek to understand the dynamics of this interaction to reach better co-existence in fairness, inclusion, and aesthetics.

T owards an Angry-Birds-Like Game System for Promoting Mental Well-Being of Players Using Art-Therapy-Embedded Procedural Content Generation Zhou Fang 1, Pujana Paliyawan 2, Ruck Thawonmas 1 and Tomohiro Harada 1 1 College of Information Science and Engineering 2 Research Organization of Science and Technology Ritsumeikan University, Japan ruck@is.ritsumei.ac.jp Abstract -- This paper presents an integration of a game system and the art therapy concept for promoting the mental wellbeing of video game players. In the proposed game system, the player plays an Angry-Birds-like game in which levels in the game are generated based on images they draw. Upon finishing a game level, the player also receives positive feedback (praising words) toward their drawing and the generated level from an Art Therapy AI. The proposed system is composed of three major parts: (1) a drawing recognizer that identifies what object is drawn by the player (Sketcher), (2) a level generator that converts the drawing image into a pixel image, then a set of blocks representing a game level (PCG AI), and (3) the Art Therapy AI that encourages the player and improves their emotion. This paper describes an overview of the system and explains how its major components function.