Instructional gestures are essential for teaching, enhancing communication and student comprehension. Current training methods for developing these skills can be time-consuming, isolating, or overly prescriptive, e.g., watching lengthy, one-size-fits-all videos. Conversely, research suggests that developing these tacit, experiential skills requires teachers’ peer learning, where they learn from each other and build shared knowledge. While much HCI exploration has applied learning-by-teaching to students’ peer learning, little has explored this approach for teacher professionalization. We present Novobo, an apprentice AI-agent stimulating teachers’ peer learning of instructional gestures through verbal and bodily inputs. An evaluation with 30 teachers in 10 collaborative sessions showed Novobo prompted teachers to externalize and share tacit knowledge through dialogue and movement. Teaching an AI mentee together reduced their pressure, facilitating peer exchange and the co-construction of practical knowledge. This work contributes a novel design and empirical insights into how teachable AI-agents can facilitate peer learning in teacher professionalization.

Content Warning: This paper contains sexually explicit and violent images and text. User-created chatbots powered by generative AI offer new ways to share and interact with Not-Safe-For-Work (NSFW) content. However, little is known about the characteristics of these GenAI-based chatbots and their user interactions. Drawing on the functional theory of NSFW on social media, this study analyzes 376 NSFW chatbots and 307 public conversation sessions on FlowGPT. Findings identify four chatbot types: roleplay characters, story generators, image generators, and do-anything-now bots. AI Characters portraying fantasy personas and enabling hangout-style interactions are most common, often using explicit avatar images to invite engagement. Sexual, violent, and insulting content appears in both user prompts and chatbot outputs, with some chatbots generating explicit material even when users do not create erotic prompts. In sum, the NSFW experience on FlowGPT can be understood as a combination of virtual intimacy, sexual delusion, violent thought expression, and unsafe content acquisition. We conclude with implications for chatbot design, creator support, user safety, and content moderation.

Educational drama is a proven method for anti-bullying education, but its traditional reliance on teachers and peers limits its accessibility to children and families outside of school. HCI has rarely explored how to augment this practice with AI-infused, interactive role-playing or how to involve parents in the process. We introduce DOLLama, an AI-powered projection-augmented interactive system that transforms children’s toys and family-created stories into gamified anti-bullying vignettes. A study with 20 families demonstrated how DOLLama facilitated children’s and parents’ learning. Children used their toys to enact the roles of the one being bullied and bystanders, developing empathy and practicing coping strategies in co-performance with AI-controlled toy characters. By observing this play, parents gained new insights into their child’s strengths and challenges and identified their own knowledge gaps. Based on these findings, we derive HCI design implications for AI-enhanced, toy-mediated educational drama that supports anti-bullying education for children and their families.

To overcome the lack of deep personalization in standard biofeedback methods, we introduce ASafePlace, a system utilizing an AI-powered, art-therapy-inspired exercise called The Safe Place, to create a personalized VR biofeedback experience. In our system, users sketch a personal sanctuary from memory, which is then transformed into a customized 360° virtual environment with personalized audio guidance for relaxation training. A study with 52 participants showed this approach effectively reduced anxiety and increased user presence, while the integration of art-therapy-inspired activity and biofeedback produced strong improvements in physiological relaxation, measured by heart rate variability and respiration rate. Qualitative results showed how participants’ sense of familiarity and presence was enhanced by the symbolic elements and natural sanctuaries created from their autobiographical memories. Our findings demonstrate that art-therapy-inspired activity is a powerful tool for creating highly effective and individualized relaxation experiences, naturally connecting the virtual environment to a user’s core memories and emotions.

Robotic teaching assistants (TAs) often use body-mounted screens to deliver content. In nomadic, walk-and-talk learning, such as tours in makerspaces, these screens can distract learners from real-world objects, increasing extraneous cognitive load. HCI research lacks empirical comparisons of potential alternatives, such as robots with in-situ projection versus screen-based counterparts; little knowledge has been derived for designing such alternatives. We introduce ProjecTA, a semi-humanoid, gesture-capable TA that guides learners while projecting near-object overlays coordinated with speech and gestures. In a mixed-method study (N=24) in a university makerspace, ProjecTA significantly reduced extraneous load and outperformed its screen-based counterpart in perceived usability, usefulness of visual display, and cross-modal complementarity. Qualitative analyses revealed how ProjecTA’s coordinated projections, gestures and speech anchored explanations in place and time, enhancing understanding in ways a screen could not. We derive key design implications for future robotic TAs leveraging spatial projection to support mobile learning in physical environments.

Agentic AI holds promise for usability testing, yet its role as an audio moderator in think-aloud protocols is not well understood. This study explores: (1) how to design and develop an agentic audio moderator for think-aloud usability testing, and (2) how participants moderated by an agentic moderator differ from those moderated by a human regarding task performance, verbalization behaviors, user experience, and social perceptions of the moderator. Using a design-based research approach, we interviewed nine UX experts, iteratively developed an AI moderator, and evaluated it in a randomized controlled trial (N = 60) with a note-taking application. Results suggest that significant differences were not observed between AI and human moderators in task performance or verbalization behaviors, though AI moderators received lower social perception ratings. This work contributes the first design-oriented evaluation of AI moderators in usability testing, offering implications for developing more acceptable and effective agentic audio moderators.

Danmaku, users' live comments synchronized with, and overlaying on videos, has recently shown potential in promoting online video-based learning.However, user-generated danmaku can be scarce-especially in newer or less viewed videos-and its quality is unpredictable, limiting its educational impact.This paper explores how large multimodal models (LMM) can be leveraged to automatically generate effective, high-quality danmaku.We first conducted a formative study to identify the desirable characteristics of contentand emotion-related danmaku in educational videos.Based on the obtained insights, we developed ClassComet, an educational video platform with novel LMM-driven techniques for generating relevant types of danmaku to enhance video-based learning.Through user studies, we examined the quality of generated danmaku and their influence on learning experiences.The results indicate that our generated danmaku is comparable to human-created ones, and videos with both content-and emotion-related danmaku showed significant improvement in viewers' engagement and learning outcome.

Figure 1: A design novice uses Infuencer to ideate and make promotional posts to promote their homemade juice.Infuencer has the following core features: (A) The user can input a topic via a text block and explores the related images and captions in three dimensions.(B) Context-aware exploration is supported which updates the image and caption recommendation by dragging a brand/product image or message to the initial image and caption recommendation.(C) Various materials (i.e., image and text) can be fexibly fused to make a new image or caption.(D) Infuencer allows the user to not only easily create harmonious promotional posts but also quickly obtain multiple post alternatives.Steps in (A), (B), and (C) can be fexibly combined or skipped; as soon as the user fnds satisfed image and/or caption, they can go to (D) for post generation.

Procrastination, the voluntary delay of tasks despite potential negative consequences, has prompted numerous time and task management interventions in the HCI community. While these interventions have shown promise in addressing specific behaviors, psychological theories suggest that learning about procrastination itself may help individuals develop their own coping strategies and build mental resilience. However, little research has explored how to support this learning process through HCI approaches. We present ProcrastiMate, a text adventure game where players learn about procrastination's causes and experiment with coping strategies by guiding in-game characters in managing relatable scenarios. Our field study with 27 participants revealed that ProcrastiMate facilitated learning and self-reflection while maintaining psychological distance, motivating players to integrate newly acquired knowledge in daily life. This paper contributes empirical insights on leveraging serious games to facilitate learning about procrastination and offers design implications for addressing psychological challenges through HCI approaches.

Classroom debates are a unique form of collaborative learning characterized by fast-paced, high-intensity interactions that foster critical thinking and teamwork. Despite the recognized importance of debates, the role of AI tools, particularly LLM-based systems, in supporting this dynamic learning environment has been under-explored in HCI. This study addresses this opportunity by investigating the integration of LLM-based AI into real-time classroom debates. Over four weeks, 22 students in a Design History course participated in three rounds of debates with support from ChatGPT. The findings reveal how learners prompted the AI to offer insights, collaboratively processed its outputs, and divided labor in team-AI interactions. The study also surfaces key advantages of AI usage - reducing social anxiety, breaking communication barriers, and providing scaffolding for novices - alongside risks, such as information overload and cognitive dependency, which could limit learners' autonomy. We thereby discuss a set of nuanced implications for future HCI exploration.

Despite being prominent and ubiquitous, message-based communication is limited in nonverbally conveying emotions. Besides emoticons or stickers, messaging users continue seeking richer options for affective communication. Recent research explored using chat-balloons’ shape and color to communicate emotional states . However, little work explored whether and how chat-balloon animations could be designed to convey emotions. We present the design of AniBalloons, 30 chat-balloon animations conveying Joy, Anger, Sadness, Surprise, Fear, and Calmness. Using AniBalloons as a research means, we conducted three studies to assess the animations’ affect recognizability and emotional properties ( N = 40 ), and probe how animated chat-balloons would influence communication experience in typical scenarios including instant messaging ( N = 72 ) and chatbot service ( N = 70 ). Our exploration contributes a set of chat-balloon animations to complement nonverbal affective communication for a range of text-message interfaces, and empirical insights into how animated chat-balloons might mediate particular conversation experiences (e.g., perceived interpersonal closeness, or chatbot personality).

Dental anxiety is prevalent among children, often leading to missed treatment and potential negative effects on their mental well-being. While several interventions (e.g., pharmacological and psychotherapeutic techniques) have been introduced for anxiety alleviation, the recently emerged virtual reality (VR) technology, with its immersive and playful nature, opened new opportunities for complementing and enhancing the therapeutic effects of existing interventions. In this light, we conducted a series of co-design workshops with 13 children aged 10-12 to explore how they envisioned using VR to address their fear and stress associated with dental visits, followed by interviews with parents (n = 13) and two dentists. Our findings revealed that children expected VR to provide immediate relief, social support, and a sense of control during dental treatment, parents and dentists prioritized treatment efficiency and safety issues. Drawing from the findings, we discuss the considerations of multi-stakeholders for developing VR-assisted anxiety management applications for children within and beyond dental settings.

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored “Emotional Teasers”—pre-retrieval cues offering a glimpse into an awaiting message’s emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders’ choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.

Storymaking, as an integrative form of expressive arts therapy, is an effective means to foster family communication. Yet, the integration of generative AI as expressive materials in therapeutic storymaking remains underexplored. And there is a lack of HCI implications on how to support families and therapists in this context. Addressing this, our study involved five weeks of storymaking sessions with seven families guided by a professional therapist. In these sessions, the families used both traditional art-making materials and image-based generative AI to create and evolve their family stories. Via the rich empirical data and commentaries from four expert therapists, we contextualize how families creatively melded AI and traditional expressive materials to externalize their ideas and feelings. Through the lens of Expressive Therapies Continuum (ETC), we characterize the therapeutic implications of AI as expressive materials. Desirable interaction qualities to support children, parents, and therapists are distilled for future HCI research.

In both remote and physical work environments, it is commonplace for help-seeking messages to be rejected by other colleagues. This paper investigates how signifying co-workers’ stress status would influence the social diction and empathy of help-seekers in the context of rejection. 36 participants were recruited to perform help-seeking tasks with virtual co-workers via a professional mobile messaging application (Trillian). Their device was tailored with a vibrotactile mechanism (TacStatus), which could signify different emotional states of the co-workers: no-cue, relaxed, normal, and stressful. Independent sample Friedman nonparametric tests were conducted to analyze the social diction and empathy of the participants in their messages for help-seeking and responses to the co-workers’ rejection. This study revealed that stress cues have observable impacts on the social diction and empathy of help-seekers. Stressful and relaxed cues were found to evidently shape the social diction of help-seekers. When faced with a relaxed co-worker, the help-seeker felt disappointed and unaccepted after being rejected. By contrast, when confronted with a stressful cue, help-seekers tended to exhibit relatively more positive emotions after been rejected. This study attempts to reveal the mechanism through which stress cues influence professional messaging interactions and collaboration. The findings could provide implications for the design of socio-emotional cues in the context of messaging.

Collective stress is the stress within a group or an organization. It affects individuals’ well-being and group productivity. HCI research has started exploring collective stress visualization to facilitate group awareness and collective coping via testing prototypes in controlled settings. However, an in-depth understanding of users’ needs and envisaged scenarios based on their authentic experiences are still lacking. In this study, we utilized a participatory approach called co-constructing stories to investigate how a collective stress visualization would be used in office workers’ authentic workday routines. We constructed use case stories with a group of office workers separately based on their personal lived experiences, using a design probe called AffectiveGarden. Our results categorized six clusters of benefits for collective coping through visualization and their implications for future design practice.

A rising number of HCI scholars have begun to use materiality as a starting point for exploring the design's potential and restrictions. Despite the theoretical flourishing, the practical design process and instruction for beginner practitioners are still in scarcity. We leveraged the pictorial format to illustrate our crafting process of Puffy, a bio-inspired artifact that features a cilia-mimetic surface expressing anthropomorphic qualities through shape changes. Our approach consists of three key activities (i.e., analysis, synthesis, and detailing) interlaced recursively throughout the journey. Using this approach, we analyzed different input sources, synthesized peers’ critiques and self-reflection, and detailed the designed experience with iterative prototypes. Building on a reflective analysis of our approach, we concluded with a set of practical implications and design recommendations to inform other practitioners to initiate their investigations in interactive materiality.

Emoticons are indispensable in online communications. With users’ growing needs for more customized and expressive emoticons, recent messaging applications begin to support (limited) multi-modal emoticons:, enhancing emoticons with animations or vibrotactile feedback. However, little empirical knowledge has been accumulated concerning how people create, share and experience multi-modal emoticons in everyday communication, and how to better support them through design. To tackle this, we developed VibEmoji, a user-authoring multi-modal emoticon interface for mobile messaging. Extending existing designs, VibEmoji grants users greater flexibility to combine various emoticons, vibrations, and animations on-the-fly, and offers non-aggressive recommendations based on these components’ emotional relevance. Using VibEmoji as a probe, we conducted a four-week field study with 20 participants, to gain new understandings from in-the-wild usage and experience, and extract implications for design. We thereby contribute to both a novel system and various insights for supporting users’ creation and communication of multi-modal emoticons.

Despite the digital revolution, physical space remains the site for teaching and learning embodied knowledge and skills. Both teachers and students must develop spatial competencies to effectively use classroom spaces, enabling fluid verbal and non-verbal interaction. While video permits rich activity capture, it provides no support for quickly seeing activity patterns that can assist learning. In contrast, position tracking systems permit the automated modelling of spatial behaviour, opening new possibilities for feedback. This paper introduces the design rationale for ”Dandelion Diagrams” that integrate participant location, trajectory and body orientation over a variable period. Applied in two authentic teaching contexts (a science laboratory, and a nursing simulation) we show how heatmaps showing only teacher/student location led to misinterpretations that were resolved by overlaying Dandelion Diagrams. Teachers also identified a variety of ways they could aid professional development. We conclude Dandelion Diagrams assisted sensemaking, but discuss the ethical risks of over-interpretation.

Almost all older people with dementia have progressive communication difficulties, which lead to increased social isolation and negative emotions. Thus, providing communication assistance for them is essential. This paper explores the feasibility of using social robots to assist older people with dementia in their face-to-face communication with others. We designed the behavior of a humanoid Pepper robot and made a Wizard of Oz prototype that the robot can serve as a personal memory assistant. The robot stores personal information for older people and assists in their communication through voice and screen display. In a video-based study with 88 participants, we investigated the effects of this assistive robot from a third-person observer perspective. Data were collected and analyzed using both three-way MANCOVAs for quantitative analysis and conventional content analysis for qualitative data. The results revealed that, by providing memory support, the robot significantly improved the observer's perceptions of an older person with dementia, including her perceived communication ability and performance, and personal image. Meanwhile, the communication is perceived to be significantly more effective when the robot assisted an older person. The willingness of others to communicate with more senior people also increased accordingly. Based on these findings, we present guidelines that may inform the design and development of communication assistant robots for older people with dementia.

Slow breathing guiding applications increasingly emerge, showing promise for helping knowledge workers to better cope with workaday stress. However, standard breathing guidance is non-interactive, with rigid paces. Despite their effects being proved, they could cause respiratory fatigue, or lack of training motivation, especially for novice users. To explore new design possibilities, we investigate using heart rate variability (HRV) data to mediate breathing guidance, which results in two HRV-enhanced guidance modes: (i) responsive breathing guidance and (ii) adaptive breathing guidance. These guidance modes are implemented on a soft haptic interface named “ViBreathe”. We conducted a user test (N = 24), and a one-week field deployment (N = 4) with knowledge workers, to understand the user experience of our design. The HRV-enhanced modes were generally experienced to reduce tiresome and improve engagement and comfort. And Vibreathe showed great potential for seamlessly weaving slow breathing practice into work routines. We thereby summarize related design insights and opportunities.

Effective teamwork is critical to improve patient outcomes in healthcare. However, achieving this capabilityrequires that pre-service nurses develop the spatial abilities they will require in their clinical placements, suchas: learning when to remain close to the patient and to other team members; positioning themselves correctlyat the right time; and deciding on specific team formations (e.g. face-to-face or side-by-side) to enable effectiveinteraction or avoid disrupting clinical procedures. However, positioning dynamics are ephemeral and caneasily become occluded by the multiple tasks nurses have to accomplish. Digital traces automatically capturedby indoor positioning sensors can be used to address this problem for the purpose of improving nurses' reflection, learning and professional development. This paper presents; i) a qualitative study that illustrateshow to elicit spatial behaviours from educators' pedagogical expectations, and ii) a modelling approachthat transforms nurses' low-level position traces into higher-order proxemics constructs, informed by sucheducatos' expectations, in the context of simulation-based teamwork training. To illustrate our modellingapproach, we conducted an in-the-wild study with 55 undergraduate students and five educators from whompositioning traces were captured in eleven authentic nursing education classes. Low-levelx-ydata was usedto model three proxemic constructs: i) co-presence in interactional spaces, ii) socio-spatial formations (i.e.f-formations), and ii) presence in spaces of interest. Through a number of vignettes, we illustrate how indoorpositioning analytics can be used to address questions that educators and researchers have about teamwork inhealthcare simulation settings.

Teachers' response to the real-time needs of diverse learners in the classroom is important for each learner's success. Teachers who give differentiated instruction (DI) provide pertinent support to each student and acknowledge their differences in learning style and pace. However, due to the already complex and intensive routines in classrooms, it is demanding and time-consuming for teachers to implement DI on-the-spot. This study aims to explore how to ease teachers' classroom differentiation by enabling effortless, low-threshold student-teacher communications through a peripheral interactive system. Namely, we present a six-week study, in which we iteratively co-designed and field-tested interaction solutions with eight school teachers, using a set of distributed, interactive LED-objects (the 'FireFlies' platform). By connecting our findings to the theories of DI, we contribute empirical knowledge about the advantages and limitations of a peripheral interactive system in supporting DI. Taken together, we summarize concrete opportunities and recommendations for future design.

Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design.

This pictorial proposes an alternative mode of interaction between nurses and clinical alarm syste ms and shows how three concepts, developed to interact through the end user's periphery of attention, can be applied in a clinical setting to improve the workflow of nurses and wellbeing of patients.

Through the integration of technology-enhanced learning (TEL) in the classrooms, there is an increase in Virtual Learning Environment-supported classes in secondary schools, which brings unintentional complexities in terms of monitoring for teachers [25]. To support secondary school teachers during VLE-supported lessons, a peripheral data visualisation system was designed and implemented in a three-week field study. Both qualitative and quantitative data were gathered and analysed through methodological triangulation in order to get an in-depth understanding about the use of the system by teachers. The key findings from our study were that the peripheral data visualisation tool, by being a distributed, highly visible system, was well integrated in the teachers' practice. The peripheral visualisation served as a trigger for teacher interventions where the teacher could confront the student's level of concentration and provide support when a student needs it. Furthermore, by offloading the secondary tasks of checking the students' level of concentration and progress to the visualisation, most teachers experienced more peace of mind and space to manage their primary teaching practice. Lastly, approximately 95% of 89 students experienced the data visualisation as neutral or motivating, while 5.7% of the students experienced violation of privacy by this medium.

Reflecting on their performance during classroom-teaching is an important competence for teachers. Such reflection-in-action (RiA) enables them to optimize teaching on the spot. But RiA is also challenging, demanding extra thinking in teachers' already intensive routines. Little is known on how HCI systems can facilitate teachers' RiA during classroom-teaching. To fill in this gap, we evaluate ClassBeacons, a system that uses spatially distributed lamps to depict teachers' ongoing performance on how they have divided their time and attention over students in the classroom. Empirical qualitative data from eleven teachers in 22 class periods show that this ambient information facilitated teachers' RiA without burdening teaching in progress. Based on our theoretical grounding and field evaluation, we contribute empirical knowledge about how an HCI system enhanced teachers' process of RiA as well as a set of design principles for unobtrusively supporting RiA.

As necessary for creating a learner-centered environment, nowadays teachers are expected to be more mindful about their proximity distribution: how to spend time in different locations of the classroom with individual learners. However feedback on this is only given to teachers by experts after classroom observation. In this paper we present the design and evaluation of ClassBeacons, a novel ambient information system that visualizes teachers' physical proximity through tangible devices distributed over the classroom. An expert review and a field evaluation with eight secondary school teachers were conducted to explore potential values of such a system and gather user experiences. Results revealed rich insights into how the system could influence teaching and learning, as well as how a distributed display can be seamlessly integrated into teachers' routines.