Understanding Reader Perception Shifts upon Disclosure of AI AuthorshipAs AI writing support becomes ubiquitous, the question of how disclosing its use affects reader perception remains critical and underexplored. We conducted a controlled study with 261 participants to examine how disclosing varying levels of AI involvement shifts perceptions of the author across six distinct communicative acts. Our analysis of 990 evaluations reveals that disclosure generally erodes perceived trustworthiness, caring, competence, and likability, with the most precipitous declines observed in social and interpersonal writing. A thematic analysis of participant feedback attributes these negative shifts to a perceived loss of human sincerity, diminished authorial effort, and the contextual inappropriateness of AI. Notably, however, we find that higher AI literacy mitigates these negative perceptions, leading to greater tolerance or even appreciation for AI assistance. Our results highlight the nuanced social dynamics of AI-mediated authorship and inform design implications for transparent, context-sensitive writing systems that better preserve trust and authenticity.2026HNHiroki Nakano et al.The University of TokyoGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationAI Ethics, Fairness & AccountabilityIUI
LLM-based In-situ Thought Exchanges for Critical Paper ReadingCritical reading is a primary way through which researchers develop their critical thinking skills. While exchanging thoughts and opinions with peers can strengthen critical reading, junior researchers often lack access to peers who can offer diverse perspectives. To address this gap, we designed an in-situ thought exchange interface informed by peer feedback from a formative study (N=8) to support junior researchers’ critical paper reading. We evaluated the effects of thought exchanges under three conditions (no-agent, single-agent, and multi-agent) with 46 junior researchers over two weeks. Our results showed that incorporating agent-mediated thought exchanges during paper reading significantly improved participants’ critical thinking scores compared to the no-agent condition. In the single-agent condition, participants more frequently made reflective annotations on the paper content. In the multi-agent condition, participants engaged more actively with agents’ responses. Our qualitative analysis further revealed that participants compared and analyzed multiple perspectives in the multi-agent condition. This work contributes to understanding in-situ AI-based support for critical paper reading through thought exchanges and offers design implications for future research.2026XFXinrui Fang et al.The University of TokyoHuman-LLM CollaborationUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingIUI
What We Talk About When We Talk About Frameworks in HCIIn HCI, frameworks function as a type of theoretical contribution, often supporting ideation, design, and evaluation. Yet, little is known about how they are actually used, what functions they serve, and which scholarly practices that shape them. To address this gap, we conducted a systematic review of 615 papers from a decade of CHI proceedings (2015-2024) that prominently featured the term framework. We classified these papers into six engagement types. We then examined the role, form, and essential components of newly proposed frameworks through a functional typology, analyzing how they are constructed, validated, and articulated for reuse. Our results show that enthusiasm for proposing new frameworks exceeds the willingness to iterate on existing ones. They also highlight the ambiguity in the function of frameworks and the scarcity of systematic validation. Based on these insights, we call for more rigorous, reflective, and cumulative practices in the development and use of frameworks in HCI.2026SFShitao Fang et al.The University of TokyoParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)Research Ethics & Open ScienceCHI
Can AI be a Social Buffer? Investigating the Effect of AI-assisted Cognitive Reappraisal and Narrative Perspectives on Managing Difficult Workplace Conversations over EmailIn difficult workplace email conversations, such as layoffs or resource negotiations, the absence of nonverbal cues can exacerbate negative emotions experienced by recipients. While existing tools support senders in refining tone, there is little support for processing emotionally intensive content from the receivers' side. This study investigated the use of large language models that added positive or neutral reframings, written in either first or third person, to original emails, with the aim of helping recipients view difficult conversations in a different light. In a controlled study with 132 participants, positive reframing reduced receivers' negative emotions and was rated as more helpful than neutral reframing, regardless of narrative perspective. Although reframing type did not significantly change conflict management behaviors, positive reframing led to fewer power-related words in interpretations of the email. These findings highlight opportunities and challenges for designing AI as a social buffer to facilitate difficult conversations online.2026CYChi-Lan Yang et al.The University of TokyoHuman-LLM CollaborationAffective Feedback & Emotion Regulation InterfacesAffective Human-Computer DialogueCHI
Conversational Inoculation to Enhance Resistance to MisinformationProliferation of misinformation is a globally acknowledged problem. Cognitive Inoculation helps build resistance to different forms of persuasion, such as misinformation. We investigate Conversational Inoculation, a method to help people build resistance to misinformation through dynamic conversations with a chatbot. We built a Web-based system to implement the method, and conducted a within-subject user experiment to compare it with two traditional inoculation methods. Our results validate Conversational Inoculation as a viable novel method, and show how it was able to enhance participants' resistance to misinformation.A qualitative analysis of the conversations between participants and the chatbot highlighted adaptability, independence, trust and friction as the main factors affecting Conversational Inoculation.We discuss the opportunities and challenges of using Conversational Inoculation to combat misinformation. Our work contributes a timely investigation and a promising research direction in scalable ways to combat misinformation.2026DSDániel Szabó et al.University of OuluConversational ChatbotsMisinformation & Fact-CheckingCHI
When Group Spirit Meets Personal Journeys: Exploring Motivational Dynamics and Design Opportunities in Group TherapyPsychotherapy, such as cognitive-behavioral therapy (CBT), is effective in treating various mental disorders. Technology-facilitated mental health therapy improves client engagement through methods like digitization or gamification. However, these innovations largely cater to individual therapy, ignoring the potential of group therapy—a treatment for multiple clients concurrently, which enables individual clients to receive various perspectives in the treatment process and also addresses the scarcity of healthcare practitioners to reduce costs. Notwithstanding its cost-effectiveness and unique social dynamics that foster peer learning and community support, group therapy, such as group CBT, faces the issue of attrition. While existing medical work has developed guidelines for therapists, such as establishing leadership and empathy to facilitate group therapy, understanding about the interactions between each stakeholder is still missing. To bridge this gap, this study examined a group CBT program called the Serigaya Methamphetamine Relapse Prevention Program (SMARPP) as a case study to understand stakeholder coordination and communication, along with factors promoting and hindering continuous engagement in group therapy. In-depth interviews with eight facilitators and six former clients from SMARPP revealed the motivators and demotivators for facilitator-facilitator, client-client, and facilitator-client communications. Our investigation uncovers the presence of discernible conflicts between clients' intrapersonal motivation as well as interpersonal motivation in the context of group therapy through the lens of self-determination theory. We discuss insights and research opportunities for the HCI community to mediate such tension and enhance stakeholder communication in future technology-assisted group therapy settings.2025SGShixian Geng et al.Caring at a DistanceCSCW
Examining Input Modalities and Visual Feedback Designs in Mobile Expressive WritingExpressive writing is an established approach for stress management. Recently, information technologies, such as smartphones, have also been explored for expressive writing. Although mobile interfaces have the potential to support various daily writing activities, interface designs for mobile expressive writing and their effects on stress relief still lack empirical understanding. We examined the interface design of mobile expressive writing by investigating the influence of input modalities and visual feedback designs on usability and perceived cathartic effects through field studies. While our studies confirmed the stress-relieving effects of mobile expressive writing, our results offer important insights into interface design. We found keyboard-based text entry more suited and preferred over voice messages for its privacy and reflective nature. Participants expressed different reasons for preferring different post-writing visual feedback depending on the cause and type of stress. This work advances interface design for mobile expressive writing and deepens understanding of its effects.2025SNShunpei Norihama et al.Voice User Interface (VUI) DesignMental Health Apps & Online Support CommunitiesMobileHCI
Beyond the Dialogue: Multi-chatbot Group Motivational Interviewing for Premenstrual Syndrome (PMS) ManagementPremenstrual syndrome (PMS) is a prevalent disorder among women, often exacerbated by a lack of peer support due to associated stigmatization. Drawing inspiration from the established benefits of group therapy, particularly the sense of belonging it fosters, we developed a multi-chatbot group motivational interviewing system. The system consists of a facilitator bot and two peer bots, and simulates a group counseling environment for PMS management using Large Language Models (LLMs). We conducted a study with 63 participants and divided them into three conditions (no intervention, 1-on-1 chatbot, group chatbots) over two menstruation cycles for evaluation. Our findings revealed that participants in the group chat condition exhibited higher levels of engagement and language convergence with the chatbots. These participants were also able to engage in social learning and demonstrated motivation in coping through interactions with the chatbots. Finally, we discuss design implications for multi-chatbot interactions in supporting mental health.2025SGShixian Geng et al.The University of Tokyo, Interactive Intelligent Systems LaboratoryConversational ChatbotsHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
DIPA2: An Image Dataset with Cross-cultural Privacy Perception AnnotationsXu 等人构建 DIPA2 图像数据集，提供跨文化隐私感知标注，为隐私保护研究提供重要的数据资源。2024AXAnran Xu et al.Privacy Perception & Decision-MakingUbiComp
Examining Human Perception of Generative Content Replacement in Image Privacy ProtectionThe richness of the information in photos can often threaten privacy, thus image editing methods are often employed for privacy protection. Existing image privacy protection techniques, like blurring, often struggle to maintain the balance between robust privacy protection and preserving image usability. To address this, we introduce a generative content replacement (GCR) method in image privacy protection, which seamlessly substitutes privacy-threatening contents with similar and realistic substitutes, using state-of-the-art generative techniques. Compared with four prevalent image protection methods, GCR consistently exhibited low detectability, making the detection of edits remarkably challenging. GCR also performed reasonably well in hindering the identification of specific content and managed to sustain the image's narrative and visual harmony. This research serves as a pilot study and encourages further innovation on GCR and the development of tools that enable human-in-the-loop image privacy protection using approaches similar to GCR.2024AXAnran Xu et al.the University of TokyoGenerative AI (Text, Image, Music, Video)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
SoundTraveller: Exploring Abstraction and Entanglement in Timbre Creation Interfaces for SynthesizersTimbre exploration and creation are key tasks in electronic music composition. Modern synthesizers can produce thousands of unique timbres, but this complexity hinders musicians' ability to explore these timbres effectively. We contribute SoundTraveller, an interactive timbre exploration system aimed at fostering electronic musicians' creative processes. SoundTraveller allows the user to explore the timbral space using two modes: evolutionary and morphing, with which they can generate hundreds of unique timbres without the need to edit individual parameters. Our user study confirmed that SoundTraveller supported participants' exploration, decreased their cognitive load, and increased their perceived creativity. Through analysis of our interview study, we contribute design considerations for timbre exploration systems, and for exploring large aesthetic parameter spaces more generally. Finally, we discuss how systems like SoundTraveller can fit into the existing workflows of electronic music composers, and how shared agency with creative technologies can impact the creative process.2023ZSZefan Sramek et al.Music Composition & Sound Design ToolsDIS
Groupnamics: Designing an Interface for Overviewing and Managing Parallel Group Discussions in an Online ClassroomInstructors facilitating online classes have a limited ability to see and hear interactions of student groups working in parallel, which prevents them from interacting with students effectively. In this work, we explore interface design for providing an overview of parallel group discussions in online classrooms. We derive design considerations through a participatory design process and instantiate them in our visualization interface, Groupnamics. Groupnamics visualizes recent vocal activities and discussion statuses of each group in a one-page view, facilitating identification of groups where intervention may be needed. Our user study with 16 instructors confirmed that Groupnamics can successfully provide cues for when instructors should join group discussions and improvements on the perceived usefulness and ease of use over the baseline interface representing existing videoconferencing tools. Our qualitative results suggest future research directions in interface design for online parallel group discussions.2023ASArissa J. Sato et al.The University of TokyoOnline Learning & MOOC PlatformsCollaborative Learning & Peer TeachingCHI
Gesture-aware Interactive Machine Teaching with In-situ Object AnnotationsInteractive Machine Teaching (IMT) systems allow non-experts to easily create Machine Learning (ML) models. However, existing vision-based IMT systems either ignore annotations on the objects of interest or require users to annotate in a post-hoc manner. Without the annotations on objects, the model may misinterpret the objects using unrelated features. Post-hoc annotations cause additional workload, which diminishes the usability of the overall model building process. In this paper, we develop LookHere, which integrates in-situ object annotations into vision-based IMT. LookHere exploits users' deictic gestures to segment the objects of interest in real time. This segmentation information can be additionally used for training. To achieve the reliable performance of this object segmentation, we utilize our custom dataset called HuTics, including 2040 front-facing images of deictic gestures toward various objects by 170 people. The quantitative results of our user study showed that participants were 16.3 times faster in creating a model with our system compared to a standard IMT system with a post-hoc annotation process while demonstrating comparable accuracies. Additionally, models created by our system showed a significant accuracy improvement ($\Delta mIoU=0.466$) in segmenting the objects of interest compared to those without annotations.2022ZZZhongyi Zhou et al.Hand Gesture RecognitionHuman Pose & Activity RecognitionUIST
Mediating Intimacy with DearBoard: a Co-Customizable Keyboard for Everyday MessagingCo-customizations are collaborative customizations in messaging apps that all conversation members can view and change, e.g. the color of chat bubbles on Facebook Messenger. Co-customizations grant new opportunities for expressing intimacy; however, most apps offer private customizations only. To investigate how people in close relationships integrate co-customizations into their established communication app ecosystems, we built DearBoard: an Android keyboard that allows two people to co-customize its color theme and a toolbar of expression shortcuts (emojis and GIFs). In a 5-week field study with 18 pairs of couples, friends, and relatives, participants expressed their shared interests, history, and knowledge of each other through co-customizations that served as meaningful decorations, interface optimizations, conversation themes, and non-verbal channels for playful, affectionate interactions. The co-ownership of the co-customizations invited participants to negotiate who customizes what and for whom they customize. We discuss how co-customizations mediate intimacy through place-making efforts and suggest design opportunities.2021CGCarla Griggio et al.The University of Tokyo, Aarhus UniversityParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)CHI
Exploring Nudge Designs to Help Adolescent SNS Users Avoid Privacy and Safety ThreatsA nudge is a method to influence individual choices without taking away freedom of choice. We are interested in whether nudges can help adolescents avoid privacy and safety threats on social network services (SNS). We conducted an online survey to compare how 11 different nudge designs influence decisions on 9 scenarios featuring various privacy and safety threats. We collected 29,608 responses from adolescent SNS users (self-claimed high school and university students), and found that nudges can help to educe potentially risk choices. Participants were more likely to avoid potentially risky choices when presented with negative frames (e.g., "90% of users would not share a photo without permission'') than affirmative ones (e.g., "10% of users would''). Social nudges displaying statistics on how likely other people would make potentially risky decisions can have a negative effect in comparison to a nudge with only general privacy and safety suggestions. We conclude by providing design considerations for privacy/safety nudges targeting adolescent SNS users.2020HMHiroaki Masaki et al.University of TokyoPrivacy by Design & User ControlDark Patterns RecognitionCHI