Romantic couples often face challenges in navigating complex emotions and relationship dynamics through verbal communication alone, which can limit opportunities for deeper connection and understanding. To address this, we present an AI-mediated collaborative drawing system that enables couples to engage in structured drawing activities while analyzing their interactions. Inspired by interviews with art therapists, our system integrates behavioral data collection, AI-generated questions, and a comprehensive report synthesizing multimodal evidence. We conducted a user study involving 20 couples (N = 40) to evaluate the system’s effectiveness. Our findings demonstrate that the system fosters self-reflection, partner understanding, and relational awareness with high user acceptance. Participants highlighted the value of non-verbal communication as a unique pathway for gaining relational insight and deeper mutual understanding. Our work contributes design implications for AI-mediated relationship tools that position AI as a facilitator, providing accessible and creative avenues for couples to explore relational patterns and strengthen communication.

Sketch-based drawing assessments in art therapy are commonly used to understand the cognitive and psychological states of individuals. In conjunction with self-report measures, drawing assessments serve to enhance insights into an individual's psychological state. However, interpreting the drawing assessments is labor-intensive and substantially reliant on the experience of the art therapists. While a few automated approaches for analyzing drawing-based assessments have been proposed to remedy this issue, they mostly rely on existing object detection methods, where complex drawing attributes cannot be accurately decoded. To overcome these challenges, we propose a novel and comprehensive Draw-A-Person-in-the-Rain (DAPR) analysis system, CheckDAPR, which utilizes a Multimodal Large Language Model (MLLM) with object detection methods for in-depth evaluation. Our experimental results show the promising performance of CheckDAPR and its ability to reduce analysis time for art therapists, indicating its potential to aid professionals in art therapy.

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.

Sketch-based drawing assessments are useful in understanding individuals' cognitive and psychological states, such as cognitive impairment or mental disorders. Hence, these assessments have been developed and applied on a large scale, such as in schools and workplaces, to screen individuals who may require further clinical examination. However, the interpretation of a large number of drawing assessments solely relies on human experts, requiring much time and cost. To address this issue, we introduce a novel scene-level sketch dataset, SceneDAPR, which can be used to automatically analyze the drawing assessment, Draw-A-Person-in-the-Rain (DAPR), a popular psychological drawing assessment used for identifying stressful experiences and coping behavior. The proposed dataset consists of 6,420 objects depicted in 1,399 scene sketches drawn by humans, along with detailed supplementary information about the participants. SceneDAPR includes free-hand drawings from different age groups: children & adolescents, adults, and seniors. Leveraging the proposed SceneDAPR, we develop a web-based drawing assessment system. The extensive experiments demonstrate that our system shows a robust performance across the different age groups in the object detection task as well as a considerable performance compared to human experts. We believe that the proposed new sketch dataset can be used to develop an automatic system for psychological drawing assessments, which can support human experts by reducing the time and cost of analyzing the drawing assessments for a large population. SceneDAPR and experimental code are available at https://github.com/DSAIL-SKKU/SceneDAPR.

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.

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairment or mental disorders. Along with self-report measures based on a questionnaire, psychological drawing assessments can augment information about an individual psychological state. However, the interpretation of the drawing assessments requires much time and effort, especially in a large-scale group such as schools or companies, and depends on the experience of the art therapists. To address this issue, we propose an AI-based expert support system, AlphaDAPR, to support art therapists and psychologists in conducting a large-scale automatic drawing assessment. Our survey results with 64 art therapists showed that 64.06% of the participants indicated a willingness to use the proposed system. The results of structural equation modeling highlighted the importance of explainable AI embedded in the interface design to affect perceived usefulness, trust, satisfaction, and intention to use eventually. The interview results revealed that most of the art therapists show high levels of intention to use the proposed system while expressing some concerns about AI’s possible limitations and threats as well. Discussion and implications are provided, stressing the importance of clear communication about the collaborative role of AI and users.

This study proposes Magic Brush, an AI-based application for dementia prevention targeting middle-aged individuals who may be at risk for mild cognitive impairment (MCI) or dementia. In order to prevent dementia effectively at home, it is important to strengthen their intrinsic motivation. Promoting motivation is a critical issue in designing computerized cognitive therapy (CCT) for sustainability. Guided by self-determination theory, three main factors of intrinsic motivation were utilized as the main themes for the development: autonomy, competence, and relatedness. Especially, we focused on demotivating factors of low competence which are commonly shared among the elderly and developed a magic brush function equipped with neural style transfer technology to help disconnect the link between low competence and demotivation. The user study (n=35) targeting individuals aged over 50 was conducted with both quantitative and qualitative methods. In general, users reported positive experiences with Magic Brush. The results of Structural Equation Modeling analysis showed that intrinsic motivation is an important contributor to the intention to use together with perceived usefulness. Intrinsic motivation can be promoted by AI therapist likability and perception of one's own performance. Discussion and implications are provided in relation to using AI technology to promote motivation.

We present the design of Espresso, Google's SDN-based Internet peering edge routing infrastructure. This architecture grew out of a need to exponentially scale the Internet edge cost-effectively and to enable application-aware routing at Internet-peering scale. Espresso utilizes commodity switches and host-based routing/packet processing to implement a novel fine-grained traffic engineering capability. Overall, Espresso provides Google a scalable peering edge that is programmable, reliable, and integrated with global traffic systems. Espresso also greatly accelerated deployment of new networking features at our peering edge. Espresso has been in production for two years and serves over 22% of Google's total traffic to the Internet.