Although autonomous vehicles have revolutionized the transportation landscape by enabling driving without direct human intervention, it is not yet perfect. For this reason, it is critically important for users to quickly respond to takeover requests from autonomous driving agents. Based on literature on framing effects in persuasion, this study focused on the efficacy of message framing and construal level theory. An experiment (N = 78 participants) was conducted using a driving simulator, employing a 2 (message framing: gain vs. loss) × 2 (temporal distance: distant vs. close) between-subjects design. The key findings indicate that gain framing led to higher levels of perceived benefit as well as compliance and behavioral intention. In contrast, loss framing resulted in higher levels of perceived risk related to danger and prompted quicker behavioral changes, such as lower levels of distraction and faster responses to takeover requests. Conversely, construal level in the messages did not show significant differences and had an impact only on perceived risk and distraction as a moderator. Discussion and implications are provided emphasizing the importance of the messages that autonomous car agents provide.

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.

Negative feedback can have detrimental effects on the students’ self-efficacy and learning experience, yet it is inevitable for students who receive low outcomes and need improvement. This study investigates the role of pedagogical agents’ self-disclosure in providing empathy for negative feedback. An online experiment was conducted asking participants (N = 183) to interact with a voice-based pedagogical agent in a between-subjects design: 2 (feedback: positive vs. negative) X 2 (agent: self-disclose vs. non-disclose). The agent instructed students on online learning tasks and provided feedback on their task performance. Our findings showed that the agent’s self-disclosure significantly increased students’ perception of intimacy and cognitive trust toward the agent. A significant interaction effect was observed in intimacy, suggesting that the role of self-disclosure is especially pronounced when negative feedback is provided. A significant mediation effect of cognitive trust was also found between self-disclosure and feedback acceptance.

While human-like social interactions can enhance trust in and acceptance of automated vehicles (AVs), overuse may hinder these benefits, reflecting the “uncanny valley of mind” effect. We hypothesized that the AV agent’s human-like features—calling drivers by their name (Name) and expressing emotions (Emotion)—enhance trust and acceptance individually but may have adverse effects when combined. A 2 × 2 between-subjects experiment (N = 84) examined these effects. Participants in the Name and Emotion combination were more likely to perceive the experiential mind in the AV compared to the Name or Emotion conditions. However, they were less likely to show behavioral trust in the AV than in the Emotion condition, to perceive the AV as useful than in either the Name or Emotion condition, and to show intention to use the AV than in the Name condition. These findings highlight potential trade-offs in designing social AV interactions.

This study aims to identify ways to represent a conversational agent in the digital interface that can enhance older adults’ user experience focusing on both verbal (conversational form) and nonverbal factors (visual presence of conversational agent and background image). A total of 85 older adults participated in an experiment with a 2 (conversational agent: visual presence vs. no visual presence) × 2 (background image: present vs. absent) design, plus an additional condition with neither a conversational form nor manipulation of independent variables. Results highlights the importance of nonverbal factors especially environmental cues. Displaying a background image significantly increased perceived affective trust, while visual presence of the agent did not show any significant effects. Interestingly, there were interaction effects on perceived social presence, usefulness, and satisfaction. Findings also showed that using a conversational form can increase the likability, social presence, and perceived ease of use of the agent.

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.

Sticking to daily plans is essential for achieving life goals but challenging in reality. This study presents a self-voice alarm as a novel daily goal reminder. Based on the strong literature on the psychological effects of self-voice, we developed a voice alarm system that reminds users of daily tasks to support their consistent task completion. Over the course of 14 days, participants (N = 63) were asked to complete daily vocabulary tasks when reminded by an alarm (i.e., self-voice vs. other-voice vs. beep sound alarm). The self-voice alarm elicited higher alertness and uncomfortable feelings while fostering more days of task completion and repetition compared to the beep sound alarm. Both self-voice and other-voice alarms increased users’ perceived usefulness of the alarm system. Leveraging both quantitative and qualitative approaches, we provide a practical guideline for designing voice alarm systems that will foster users’ behavioral changes to achieve daily goals.

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.

Trust plays an essential role in the interaction between humans and artificial intelligence (AI). To promote trust in AI, information about the AI’s performance should be communicated well to the users. Accordingly, this paper investigates how information about AI performance should be presented, focusing on message framing and the ownership of decisions. A 2 (ownership: no ownership vs. ownership) × 3 (message framing: no information vs. negative information vs. positive information) between-subjects experiment was conducted (N = 120). Participants were asked to choose items to help them survive in the desert, supported by an AI decision. The results showed that participants without decision ownership perceived higher trust than those with decision ownership. Also, trust was perceived to be higher when participants were not given performance information than when they were. The results indicate the importance of carefully communicating with AI. The implications of this study are discussed.

Various interventions have been suggested to aid in smoking cessation. However, little is known about the effects of message framing in narratives embedded in serious games. This study compares when an individual experiences unfortunate events from smoking (i.e., loss frame) versus fortunate results benefited from smoking cessation (i.e., gain frame) in a computer game through a model (i.e., virtual self-modeling) that looks like oneself or a stranger. An experiment (N = 64) using a 2 (Message framing: Gain vs. Loss) x 2 (Modeling: Self vs. Other) between-subjects design was conducted using an anti-smoking game. Results show that the gain frame induces stronger perceived susceptibility compared to the loss frame, and self-modeling is more effective than other-modeling. Results further demonstrate that the virtual misfortune experienced through one’s own face, compared to someone else’s face, is significantly more likely to increase one’s susceptibility to the negative consequences of smoking. The study also finds a significant mediating role of identification between framing and susceptibility. Overall, by demonstrating the effectiveness of the self-modeling and gain-framed messages in gameplay, the present investigation provides meaningful contributions to the use of technology for effective health communication.

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.

Delving into motivations for and the impact of social comparison among students in the U.S. and South Korea, the present study examined cross-cultural differences in social comparison on Facebook. Following Helgeson and Mickelson [1995. “Motives for social comparison.” Personality and Social Psychology Bulletin 21 (11): 1200–1209. doi:10.1177/01461672952111008.]’s framework, social comparison was studied both offline and online based on a range of motivations rather than targets of social comparison. Results suggested an insignificant effect of culture on orientation toward social comparison. However, significant cultural differences were observed in motivations for social comparison. The U.S. participants, compared to their South Korean counterparts, demonstrated a greater propensity both offline and online to engage in social comparison motivations of self-enhancement and altruism. On Facebook, South Korean participants’ social comparison motivations for self-improvement, common bond, and self-destruction were higher than those of the U.S. participants. The U.S. participants generally felt more positive and less fatigued after making comparisons on Facebook. Factors influencing post-comparison affect were also investigated between the two countries.

Acknowledging the importance of teacher–student relationship for effective learning experiences, the present study examined the role of teacher self-disclosure and social presence in online education. An online survey was conducted from a sample of 262 undergraduate students with online class experiences. The findings suggest that students’ perception toward teacher self-disclosure increased students’ feeling of social presence about their teacher. Then, social presence in turn led to teacher–student relationship satisfaction, which ultimately increased perceived knowledge gain. Importantly, the association between teacher self-disclosure and teacher–student relationship satisfaction was mediated by social presence.