Despite the prevalence of autism spectrum disorder (ASD) and other developmental disabilities (DD) worldwide, children with ASD and DD face tremendous difficulties receiving support due to physical, financial, and psychological barriers to onsite health and education clinics. As a result, researchers and practitioners have designed software solutions aimed at providing accessible support to meet users’ needs. However, we have limited knowledge of whether these solutions indeed work in real-world settings. To address this gap, we conducted a case study on a cognitive training program called Dubupang, designed by Dubu Inc. From in-depth interviews with multiple stakeholders and field observations of children with ASD and DD, we identify Dubu Inc.’s internal development processes, the critical design issues that emerged through a series of field trials (e.g., instructional design and feedback), and the key implications (e.g., importance of caregivers’ strategic human interventions) for design that better supports both children with ASD and DD and their caregivers.

Peer Reviewed

A growing number of students in higher education are using ChatGPT for various educational purposes, ranging from seeking information to writing essays. Although many universities have officially banned the use of ChatGPT because of its potential harm and unintended consequences, it is still important to uncover how students leverage ChatGPT for learning, what challenges emerge, and how we can make better use of ChatGPT in higher education. Thus, we conducted focus group workshops and a series of participatory design sessions with thirty students who have actively interacted with ChatGPT for one semester in university and with other five stakeholders (e.g., professors, AI experts). Based on these, this paper identifies real opportunities and challenges of utilizing and designing ChatGPT for higher education.

Although the metaverse workspace has the potential to solve some of the drawbacks of remote work while maintaining its benefits, there are few real-world cases of adopting the metaverse as a legitimate workspace and fewer subsequent studies on how to design and operate the metaverse workspace. Thus, questions exist about the organizational or sociotechnical challenges that may emerge and how decisions are made when adopting and operating the metaverse workspace in a real-world setting. To answer such questions, we scrutinized the startup company Zigbang, which has completely replaced their physical office with Soma— a metaverse platform they developed where thousands of people work and other cooperative companies have moved in as tenants. By conducting field observations and semi-structured interviews with various workers and Zigbang's stakeholders, we identify essential design challenges and decisions when adopting a metaverse workspace and highlight the key takeaways learned from the company's trials and errors.

Both enterprises and their employees have globally experienced remote work at an unprecedented scale since the outbreak of COVID-19. As the pandemic becomes less of a threat, some companies have called their employees back to a physical office, citing issues related to working remotely, but many employees have refused to return. Thus, working in the metaverse has gained much attention as an alternative that could complement the weaknesses of completely remote work or even offline work. However, we do not know yet what benefits and drawbacks the metaverse has as a legitimate workspace, because there are few real cases of 1) working in the metaverse and 2) working remotely at such an unprecedented scale. Thus, this paper aims to identify real challenges and opportunities the metaverse workspace presents when compared to remote work by conducting semi-structured interviews and participatory workshops with various employees and company stakeholders (e.g., HR managers and CEOs) who have experienced at least two of three work types: working in a physical office, remotely, or in the metaverse. Consequently, we identified 1) advantages and disadvantages of remote work and 2) opportunities and challenges of the metaverse. We further discuss design implications that may overcome the identified challenges of working in the metaverse.

From initial case-reporting at the crime scene to finishing legal procedures, survivors of sexual assault navigate numerous human and technological resources provided by various organizations. During the help-seeking process, survivors unavoidably interact with multiple professional stakeholders (e.g., legal authorities) and technologies (e.g., checking their case status on a court website). In the long and complex process, survivors experience interactional breakdowns with technology, and/or humans, but few studies have explored what types of breakdowns systematically occur and how to resolve them. Thus, for this study, we conducted in-depth interviews with survivors and professionals who reside in South Korea to identify what and how breakdowns occur. Moreover, participatory design sessions were conducted with sexual assault survivors and professionals to create designs that could resolve the breakdowns. Consequently, we discovered a total of eleven breakdowns and produced solutions centered on the stakeholders (i.e., survivor and professionals). Specifically, our participants wanted an integrated system that proactively informs survivors of the holistic procedures for help-seeking and legal action, manages their case, and even interacts with legal authorities on the survivor's behalf. Based on the findings, we provide an agenda of essential designs and features that could mitigate interactional breakdowns. Additionally, we call for the HCI community to approach and solve sexual violence problems through a broad, macroscopic perspective instead of focusing on one specific technology or (social or organizational) system.

Enterprises have recently adopted AI to human resource management (HRM) to evaluate employees’ work performance evaluation. However, in such an HRM context where multiple stakeholders are complexly intertwined with different incentives, it is problematic to design AI reflecting one stakeholder group's needs (e.g., enterprises, HR managers). Our research aims to investigate what tensions surrounding AI in HRM exist among stakeholders and explore design solutions to balance the tensions. By conducting stakeholder-centered participatory workshops with diverse stakeholders (including employees, employers/HR teams, and AI/business experts), we identified five major tensions: 1) divergent perspectives on fairness, 2) the accuracy of AI, 3) the transparency of the algorithm and its decision process, 4) the interpretability of algorithmic decisions, and 5) the trade-off between productivity and inhumanity. We present stakeholder-centered design ideas for solutions to mitigate these tensions and further discuss how to promote harmony among various stakeholders at the workplace.

Recently, Artificial Intelligence (AI) has been used to enable efficient decision-making in managerial and organizational contexts, ranging from employment to dismissal. However, to avoid employees’ antipathy toward AI, it is important to understand what aspects of AI employees like and/or dislike. In this paper, we aim to identify how employees perceive current human resource (HR) teams and future algorithmic management. Specifically, we explored what factors negatively influence employees’ perceptions of AI making work performance evaluations. Through in-depth interviews with 21 workers, we found that 1) employees feel six types of burdens (i.e., emotional, mental, bias, manipulation, privacy, and social) toward AI's introduction to human resource management (HRM), and that 2) these burdens could be mitigated by incorporating transparency, interpretability, and human intervention to algorithmic decision-making. Based on our findings, we present design efforts to alleviate employees’ burdens. To leverage AI for HRM in fair and trustworthy ways, we call for the HCI community to design human-AI collaboration systems with various HR stakeholders.

Sexual assault survivors hesitate to disclose their stories to others and even avoid case-reporting because of psychological, social, and cultural reasons. Thus, conversational agents (CAs) have gained much attention as a potential counselor because CAs’ characteristics (e.g., anonymity) could mitigate various difficulties of human-human interaction (HHI). Despite the potentials, it is difficult to design a CA for survivors because various aspects should be considered. Especially, with traditional HCI approaches only (e.g., need-finding and usability tests), designers could easily miss psychological and subjective burdens that survivors feel toward a new system. Hence, while envisioning a burden-free CA for survivors, we agilely designed and implemented an initial prototype CA (NamuBot) with professionals (the police and counselors). We then conducted a qualitative user study to identify and compare burdens caused by the CA vs. humans. Lastly, we codesigned design features that could reduce the CA-bound burdens with 36 participants (19 survivors and 17 professionals). Notably, our findings showed that 17 survivors preferred reporting their case to NamuBot over humans, expressing far less burden. Although CAs could also place burdens on survivors, the burdens could be alleviated by the features that the survivors and professionals designed. Finally, we present design implications and strategies to develop burden-mitigating CAs for survivors.

The purpose of this paper is to empirically examine that which group the travelers are travelling with can have a significant impact on travelers' satisfaction with the hotel they visited to. The data was crawled and collected on Booking.com which is the most popular and well-known travel website by using our web crawler developed in Python. We analyzed 314 hotels rating data of two to five star hotels located in New York City conducting econometric analysis. Consequently, it is discovered that satisfaction of traveler groups decreases in the order of couple, friends, family, solo, and business. The group of couples expressed the highest satisfaction while the group of business travelers showed the lowest satisfaction. By conducting text analysis with 125,076 reviews, we found that such satisfaction differences can be caused by differences of travelers' experiences depending on groups.