Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self’s multiplicity.

Character journaling is a well-established exercise in actor training, but many actors struggle to sustain it due to cognitive burden, the blank page problem, and unclear short-term rewards. We reframe large language models not as co-authors but as maieutic partners—tools that guide reflection through context-aware questioning rather than producing text on behalf of the user. Based on this perspective, we designed Actor’s Note, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency. We evaluated the system in a 14-day crossover study with 29 actors using surveys, logs, and interviews. Results indicate that the tool reduced entry barriers, supported sustained reflection, and enriched character exploration, with participants describing different benefits when AI was introduced at earlier versus later rehearsal stages. This work contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.

E-commerce platforms increasingly deploy explainability features to address concerns about algorithmic opacity. However, most XAI research has focused on younger, tech-savvy users, leaving open questions about how older adults engage with these features in everyday shopping. To address this gap, we conducted a qualitative study with 20 older adults aged 60+ who regularly use NAVER Shopping, one of South Korea’s largest e-commerce platforms, examining their engagement with global (system-level) explanations, local (item-level) explanations, and a user-model dashboard. Our findings reveal that explainability does not operate uniformly. Many participants did not notice the explanation features during routine use or mistook them for advertisements. After guided interaction, global explanations elicited polarized responses: some participants deferred uncritically to algorithmic authority, whereas others dismissed the explanations as sophisticated marketing rhetoric. In contrast, local explanations grounded in users’ behavior helped recalibrate skepticism, while a user-model dashboard exposed tensions between empowerment and surveillance. Based on these findings, we propose actionable design strategies for building inclusive and adaptive XAI systems for older adults.

With the advancement of AI, even people without professional experience can create artworks using AI-based image generation systems like DALL-E 2. However, little is known about how users interact with these new AI algorithms, much less how AI-infused systems can be designed. We explore the user experience of these new technologies and their potential to foster creativity. A user study was carried out where 13 participants executed tasks of creating artworks using DALL-E 2 alongside in-depth interviews related to their experience. The results showed that users had ambivalent opinions regarding the algorithm’s performance. When users were informed of the system’s capabilities, they subsequently utilized more specific prompts to generate the intended output. Users also optimized their prompts (the queries they entered to create artworks) based on how algorithms worked to achieve their desired outcome. The users wanted a two-way interaction where AI explained the outcome and accepted feedback rather than simply accepting unilateral instructions. We discuss the implications for designing interfaces that maximize creativity while providing comfort for the users.

Large language models (LLMs) enhance productivity and creativity, but many users struggle to formulate appropriate prompts, discouraging consistent usage. We introduce PromptPilot that assists users by recommending context-appropriate prompts based on task types and the user input. We evaluated PromptPilot through an online experiment using a 3 × 3 mixed factorial design. The study involved 273 participants and examined three initiative conditions (AI-initiative, mixed-initiative, user-initiative) as a between-subjects variable, across three distinct task types (browsing, daily ideation, brainstorming) as a within-subjects variable. We found that the AI-initiative and mixed-initiative systems yielded superior performance results compared to the user-initiative system. Notably, participants in the mixed initiative generated prompts using fewer words compared to those in the AI and user-initiative. The proportion of AI-generated prompts in the AI-initiative was 2.3 times that of the mixed-initiative. We discuss implications for user interaction where AI can support users’ prompting process.

The shift to online education, accelerated by the COVID-19 pandemic, has introduced challenges in monitoring student engagement, an essential aspect of effective teaching. In response, real-time student monitoring interfaces have emerged as potential tools to aid instructors, yet their efficacy has not been thoroughly examined. Addressing this gap, we conducted a controlled experiment with 20 instructors examining the impact of engagement cues (presence versus absence) and student engagement levels (high versus low) on instructors’ monitoring effectiveness, teaching behavior adjustments, and cognitive load in online classes. Our findings underscored the fundamental benefits of student engagement monitoring interfaces for improving monitoring quality and effectiveness. Furthermore, our study highlighted the critical need for customizable interfaces that could balance the informational utility of engagement cues with the associated cognitive load and psychological stress on instructors. These insights may offer design implications for the design of future student engagement monitoring interfaces.

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.

Online dating technology mediates various social interactions for LGBTQ+ communities, yet how such technology shapes queerness remains understudied, particularly within queer women’s communities in non-Western settings. To address this gap, we conducted a qualitative study with 17 queer women, aiming to uncover their experiences and challenges in online dating within the conservative context of South Korea. Contrary to their initial expectations of exploring open-ended forms of interaction, we found that dating applications tended to systematically normalize queerness in sexuality presentation, relationship building, and shared identities in the community. These mechanisms forced them to conform to the “normalized queerness,” thereby impeding non-normative and flexible aspects of queer interactions. Building upon these findings, we discuss how the technological affordances of online dating platforms facilitate the normalization of queerness under the influence of sociocultural contexts of South Korea.

The automatic generation of sports highlight videos is emerging in both the sports entertainment domain and research community. Earlier methods for generating highlights rely on visual-audio cues or contextual cues, so they may not capture the overall flow of the game well. In this paper, we propose a technique based on Win Probability Added (WPA), an empirical sabermetric baseball statistic, to generate baseball highlights that can better reflect in-game dynamics. Additionally, we introduce methods for generating “biased” highlights toward one team by systematically manipulating WPAs. Through a mixed-method user study with 43 baseball enthusiasts, we found that participants evaluated WPA-based highlights more favorably than existing AI highlights. For (un)favorably biased highlights, the game result (win/loss) was the most dominating factor in user perception, but bias directions and strengths also had nuanced effects on them. Our work contributes to the development of automated tools for generating customized sports highlights.

Many families often live geographically apart from each other due to work, education, or marriage. Therefore, long-distance families frequently use computer-mediated communication (CMC) tools to stay connected. While CMC tools have significantly improved family communication, they cannot fully mediate social presence. To examine the potential of telepresence robots for improving long-distance family communication, we conducted a two-week qualitative in situ study involving eight families. We analyzed recorded videos of their family interactions and conducted pre- and post-deployment interviews. Our findings highlight telepresence robots’ potential as family communication tools, enabling immersive, natural, and dynamic interactions through physical embodiment and autonomy. Particularly, we identified five categories of family interaction mediated by telepresence robots: engaging in multi-party family communication, exploring home, restoring family routines, providing support, and having joint physical activities. Based on our findings, we present design guidelines for leveraging telepresence robots as effective family communication tools.

Role-playing can be an effective method for disability awareness education (DAE), and the immersive nature of virtual reality (VR) holds promise for enhancing DAE experiences. However, existing VR applications for DAE often pay less attention to the social aspects of disabilities, resulting in a lack of ability to simulate implicit social discrimination experienced by individuals with disabilities. To bridge this gap, we developed DiVRsity, a customizable VR group role-playing platform for DAE. To identify design requirements for DiVRsity, we conducted a formative study with VR and DAE experts. In an evaluation study, 28 participants engaged in role-playing exercises using DiVRsity, simulating discriminatory interpersonal situations experienced by individuals with vision impairments. Findings revealed that participants’ disability awareness significantly increased after engaging in role-playing activities using DiVRsity compared to before. We discuss the potential of VR as a role-playing platform for DAE and provide design implications for future VR-based DAE tools.

Increasingly, older adults are shopping via mobile devices as technology has been incorporated into their lives. When older adults adopt and use mobile shopping, social interactions with close others greatly influence their experience. Therefore, this paper aimed to provide a comprehensive understanding of how social interactions with close others shaped older adults’ mobile shopping practices. We conducted in-depth semi-structured interviews with 31 older adults who reported using mobile shopping regularly. We found that older adults engaged in three types of social interaction: learning from, collaborating with, and assisting close others in adopting and using mobile shopping. Through these social interactions, they gradually built trust in mobile shopping systems and supported each other’s decision-making processes. In conclusion, we presented design implications for facilitating social interactions to improve older adults’ mobile shopping experience.

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.

Many people gather online and form teams with strangers to collaborate on tasks. However, while intrateam trust and cohesion are critical for team performance, such characteristics take time to establish and are harder to build up through computer-mediated communication. Building on prior research that has shown that enhancing familiarity between members can help, we hypothesized that the use of a chatbot to support the familiarization of ad hoc teammates can help their collaboration. As such, we designed IntroBot, a chatbot that builds on an online discussion facilitator framework and leverages the social media data of users to assist their familiarization process. Through a between-subjects study (N=60), we found that participants who used IntroBot reported higher levels of trust, cohesion, and interaction quality, as well as generated more ideas in a collaborative brainstorming task. We discuss insights gained from our study, and present opportunities for the future of chatbot-assisted collaboration.

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.

The lack of reliable, personalized information often complicates sexual violence survivors’ support-seeking. Recently, there is an emerging approach to conversational information systems for support-seeking of sexual violence survivors, featuring personalization with wide availability and anonymity. However, a single best solution might not exist as sexual violence survivors have different needs and purposes in seeking support channels. To better envision conversational support-seeking systems for sexual violence survivors, we explore personalization trade-offs in designing such information systems. We implement a high-fidelity prototype dialogue-based information system through four design workshop sessions with three professional caregivers and interviewed with four self-identified survivors using our prototype. We then identify two forms of personalization trade-offs for conversational support-seeking systems: (1) specificity and sensitivity in understanding users and (2) relevancy and inclusiveness in providing information. To handle these trade-offs, we propose a reversed approach that starts from designing information and inclusive tailoring that considers unspecified needs, respectively.

Image-based sexual abuse (IBSA) is a severe social problem that causes survivors tremendous pain. IBSA survivors may encounter a lack of information and victim blame when seeking online and offline assistance. While institutions support survivors, they cannot be available 24 hours a day. Because the immediate reaction to IBSA is crucial to remove intimate images and prevent further distribution, survivors need first responders who are always accessible and do not blame them. Chatbots are constantly available, do not judge the conversation partner, and may deliver structured information and words of comfort. Therefore, we developed a chatbot to provide information and emotional support to IBSA survivors in dealing with their abuse. We analyzed nine chatbots for sexual violence survivors to identify common design elements. In addition, we sought advice from five professional counselors about the challenges survivors have while responding to their harm. We conducted a user study with 25 participants to determine the chatbot’s effectiveness in providing information and emotional support compared to internet search. The chatbot was better than the internet search regarding information organization, accessibility, and conciseness. Furthermore, the chatbot excels in providing emotional support to survivors. We discuss the survivor-centered information structure and design consideration of emotionally supportive conversation.

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.

Managing work quality has been an important issue for designing crowdsourcing tasks. Previous studies have proposed a number of ways to improve work quality, such as providing financial incentives, filtering random clickers, or designing workflow patterns. However, the potential benefit of having communication between the task owner and workers has been under-explored. This paper examined the effects of feedback and goal-setting messages on output quality in a crowdsourcing environment. The results revealed that negative and evaluative feedback and distal+proximal and achievement goal-setting messages improved output quality in shortening the volume, time, and cost to complete. The amount of monetary reward was found to have little to mixed influence over work quality, which meant more money did not lead to the higher work quality. Instead, the size of the reward must be paired with the appropriate type of messages for overall quality improvement.

Online chat functions as a discussion channel for diverse social issues. However, deliberative discussion and consensus-reaching can be difficult in online chats in part because of the lack of structure. To explore the feasibility of a conversational agent that enables deliberative discussion, we designed and developed DebateBot, a chatbot that structures discussion and encourages reticent participants to contribute. We conducted a 2 (discussion structure: unstructured vs. structured) × 2 (discussant facilitation: unfacilitated vs. facilitated) between-subjects experiment (N = 64, 12 groups). Our findings are as follows: (1) Structured discussion positively affects discussion quality by generating diverse opinions within a group and resulting in a high level of perceived deliberative quality. (2) Facilitation drives a high level of opinion alignment between group consensus and independent individual opinions, resulting in authentic consensus reaching. Facilitation also drives more even contribution and a higher level of task cohesion and communication fairness. Our results suggest that a chatbot agent could partially substitute for a human moderator in deliberative discussions.

Individuals interact with algorithms in various ways. Users even game and circumvent algorithms so as to achieve favorable outcomes. This study aims to come to an understanding of how various stakeholders interact with each other in tricking algorithms, with a focus towards online review communities. We employed a mixed-method approach in order to explore how and why users write machine non-translatable reviews as well as how those encrypted messages are perceived by those receiving them. We found that users are able to find tactics to trick the algorithms in order to avoid censoring, to mitigate interpersonal burden, to protect privacy, and to provide authentic information for enabling the formation of informative review communities. They apply several linguistic and social strategies in this regard. Furthermore, users perceive encrypted messages as both more trustworthy and authentic. Based on these findings, we discuss implications for online review community and content moderation algorithms.

Artificial intelligence (AI) algorithms are making remarkable achievements even in creative fields such as aesthetics. However, whether those outside the machine learning (ML) community can sufficiently interpret or agree with their results, especially in such highly subjective domains, is being questioned. In this paper, we try to understand how different user communities reason about AI algorithm results in subjective domains. We designed AI Mirror, a research probe that tells users the algorithmically predicted aesthetic scores of photographs. We conducted a user study of the system with 18 participants from three different groups: AI/ML experts, domain experts (photographers), and general public members. They performed tasks consisting of taking photos and reasoning about AI Mirror's prediction algorithm with think-aloud sessions, surveys, and interviews. The results showed the following: (1) Users understood the AI using their own group-specific expertise; (2) Users employed various strategies to close the gap between their judgments and AI predictions overtime; (3) The difference between users' thoughts and AI pre-dictions was negatively related with users' perceptions of the AI's interpretability and reasonability. We also discuss design considerations for AI-infused systems in subjective domains.

Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented GroupfeedBot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.

Automated journalism refers to the generation of news articles using computer programs. Although it is widely used in practice, its user experience and interface design remain largely unexplored. To understand the user perception of an automated news system, we designed NewsRobot, a research prototype that automatically generated news on major events of the PyeongChang 2018 Winter Olympic Games in real-time. It produces six types of news by combining two kinds of content (general/individualized) and three styles (text, text+image, text+image+sound). A total of 30 users participated in using NewsRobot, completing surveys and interviews on their experience. Our findings are as follows: (1) Users preferred individualized news yet considered it less credible, (2) more presentation elements were appreciated but only if their quality was assured, and (3) NewsRobot was considered factual and accurate yet shallow in depth. Based on our findings, we discuss implications for designing automated journalism user interfaces.

Augmentative and alternative communication (AAC) technologies are widely used to help non-verbal children enable communication. For AAC-aided communication to be successful, caregivers should support children with consistent intervention strategies in various settings. As such, caregivers need to continuously observe and discuss children's AAC usage to create a shared understanding of these strategies. However, caregivers often find it challenging to effectively collaborate with one another due to a lack of family involvement and the unstructured process of collaboration. To address these issues, we present TalkingBoogie, which consists of two mobile apps: TalkingBoogie-AAC for caregiver-child communication, and TalkingBoogie-coach supporting caregiver collaboration. Working together, these applications provide contextualized layouts for symbol arrangement, scaffold the process of sharing and discussing observations, and induce caregivers' balanced participation. A two-week deployment study with four groups (N=11) found that TalkingBoogie helped increase mutual understanding of strategies and encourage balanced participation between caregivers with reduced cognitive loads.

This study aims to explore the feasibility of a text-based virtual agent as a new survey method to overcome the web survey's common response quality problems, which are caused by respondents' inattention. To this end, we conducted a 2 (platform: web vs. chatbot) × 2 (conversational style: formal vs. casual) experiment. We used satisficing theory to compare the responses' data quality. We found that the participants in the chatbot survey, as compared to those in the web survey, were more likely to produce differentiated responses and were less likely to satisfice; the chatbot survey thus resulted in higher-quality data. Moreover, when a casual conversational style is used, the participants were less likely to satisfice-although such effects were only found in the chatbot condition. These results imply that conversational interactivity occurs when a chat interface is accompanied by messages with effective tone. Based on an analysis of the qualitative responses, we also showed that a chatbot could perform part of a human interviewer's role by applying effective communication strategies.

Conversational robots that exhibit human-level abilities in physical and verbal conversation are widely used in human-robot interaction studies, along with the Wizard of Oz protocol. However, even with the protocol, manipulating the robot to move and talk is cognitively demanding. A preliminary study with a humanoid was conducted to observe difficulties wizards experienced in each of four subtasks: attention, decision, execution, and reflection. Apprentice of Oz is a human-in-the-loop Wizard of Oz system designed to reduce the wizard's cognitive load in each subtask. Each task is co-performed by the wizard and the system. This paper describes the system design from the view of each subtask.

Algorithms are playing an increasingly important role in the production of news content as their computation capacity in manipulating large-scale data continues to grow. In this article, we present Personalized and Interactive News Generation System (PINGS), an algorithm-driven news generation system that is designed to provide personalized and interactive news for sports. We designed PINGS to generate baseball news based on the statistical importance of data and the direct manipulation of user interface components that alter the underlying algorithmic computation. We discuss the base-level algorithm framework for automated news content generation and describe the architecture of the system in terms of how it is designed to support the generation of personalized news stories. An evaluation revealed that the algorithm is capable of generating news stories that are significantly more interesting and pleasant to read than traditional baseball news articles.

In this paper, we present Touch+Finger, a new interaction technique that augments touch input with multi-finger gestures for rich and expressive interaction. The main idea is that while one finger is engaged in a touch event, a user can leverage the remaining fingers, the "idle" fingers, to perform a variety of hand poses or in-air gestures to extend touch-based user interface capabilities. To fully understand the use of these idle fingers, we constructed a design space based on conventional touch gestures (i.e., single- and multi-touch gestures) and inter- action period (i.e., before and during touch). Considering the design space, we investigated the possible movement of the idle fingers and developed a total of 20 Touch+Finger gestures. Using ring-like devices to track the motion of the idle fingers in the air, we evaluated the Touch+Finger gestures on both recognition accuracy and ease of use. They were classified with a recognition accuracy of over 99% and received positive and negative comments from 8 participants. We suggested 8 interaction techniques with Touch+Finger gestures that demonstrate extended touch-based user interface capabilities.

In this article, we aim to offer audiences opportunities to have an immersive experience with the statistical figures in the news. We go beyond the current numerical information representation method to develop a new system for improving the numerical experience. We implemented three different conditions for representing numerical information: 1) text, 2) infographic, 3) VR. We will observe user responses to these methods by measuring engagement, immersion and flow status to detect narrative experience. It is assumed that the VR narrative will provide a more immersive user experience. The main purpose of journalism is to deliver information that is necessary for citizens, communities, and societies to make better decisions. Statistical data is one of the essential elements to comprehend the information about themselves, the community, and society. Since the number is the core element of the statistics, our research will propose the new method to achieve the purpose of the journalism by transforming numbers into life-sized materials.

Recently drivers have greatly benefited from new vehicular technologies such as in-car navigation systems, but at the same time they can be easily distracted from those technologies. Consequently, creating displays that balance the communication of information with the attentional demand imposed on the driver is of increasing importance. A set of research has been conducted to minimize the driver’s attentional cost toward the navigational displays while driving. However, accurate evaluation methods to assess the effects of these displays in realistic environments are not yet available. This article introduces the backseat driving technique for high-fidelity, safe, and inexpensive evaluation of interaction with in-car displays. This technique makes use of a real vehicle driving on a real road. As a result it allows for the exploration of some types of research questions using audio, visual, and kinesthetic stimulus at least equal in fidelity to very high-end driving simulators, without requiring such a specialized and expensive facility. Further, it allows for the employment of detailed and fine-grained measures of attentional demand, which cannot be safely used with subjects who are actually driving. Although the backseat driving technique can address only some types of questions and so is not a full replacement for high-realism driving simulators, it may offer a new approach, which augments laboratory, simulator, and real driving for many studies. As a part of the work presented here, the backseat driving technique is used to evaluate a previously developed in-car navigation display—the MOVE system. The technique allowed for new questions to be asked, which were not able to be considered in previous laboratory studies, and for the use of study measures that were only previously able to be used in the laboratory due to driving safety concerns. Specifically, the display was shown to work well when real-world stimulus are used to navigate along a real route, reducing the navigation error rate nearly threefold, and up to sixfold when compared to displays providing more or less contextual information. In addition the display was shown to cut total display fixation time (which is time spent looking away from the road) almost in half in both cases.

The widespread use of mobile devices has transformed casual places into meeting places. However, in these places, it is uncommon to have shared information workspace such as a beam projector, making it inconvenient and inefficient to exchange information and to get a direct feedback. To address this challenge, we present Ubi-jector, a mobile system that provides a shared information space that is equally distributed to each participant's mobile device and allows group members to share documents and collaborate real-time. We first characterized the information sharing patterns and identified the limitations of the current practice in meeting places without a shared workspace, by conducting qualitative user studies. Next, we implemented Ubi-jector with the design guidelines drawn in the prior stage. Also, we performed an evaluation study that showed the possibilities of Ubi-jector to facilitate an effective information sharing and foster an active participation even in poorly-equipped environments.

The elder population is rising worldwide; in the US, no longer being able to drive is a significant marker of loss of independence. One of the approaches to helping elders drive more safely is to investigate the use of automotive user interface technology, and specifically, to explore the instrument panel (IP) display design to help attract and manage attention and make information easier to interpret.

Text-based interpersonal communication tools such as instant messenger are widely used today. These tools often feature emoticons that people use to express emotion to some degree. However, emoticons still lack the ability to communicate the details of an emotional response, such as the speaker's tone of voice or intensity of emotion. In this paper, we hypothesize that kinetic typography - text that moves or changes over time - can address some of this problem by enhancing emotional qualities of text communication using its dynamic and expressive properties.This paper presents a study showing that a small sample of designers can create kinetic effects that end-users could employ to consistently convey emotion. In the study, three designers prepared 24 kinetic examples expressing four different emotions. We found that the examples were rated quite consistently by 66 participants. These findings provide a preliminary indication that designers can create predefined kinetic effects which can be applied to a variety of textual messages, and that these effects will reliably convey a particular emotional intent. The findings from this study inform design guidelines for designing an instant messaging client that uses kinetic typography presentation.

In-vehicle navigation has changed substantially in recent years, due to the advent of computer generated maps and directions. However, these maps are still problematic, due to a mismatch between the complexity of the maps and the attentional demands of driving. In response to this problem, we are developing the MOVE (Maps Optimized for Vehicular Environments) system. This system will provide situationally appropriate map information by presenting information that uses appropriate amounts of the driver's attention. In this paper, we describe our findings of studies to help shape the design of the MOVE system, including studies on map reading and in-vehicle navigation, and studies on the effectiveness of a variety of contextually optimized route map visualizations in a simulated driving context.Results show that contextually optimized displays designed for the MOVE system should significantly reduce perceptual load in the context of driving. In our laboratory experiment there was a six-fold decrease in the total map display fixation time and nearly threefold decrease in the number of glances needed to interpret the contextually optimized display compared to a static display.