People with visual impairments (PVI) increasingly adopt conversational AI (CAI) in their daily practices. While much existing HCI research has focused on the technical capabilities of CAI, less has examined the societal contexts in which PVI use CAI, particularly from non-Western perspectives. We conducted a study with 14 participants with visual impairments in South Korea using an audio-based probe featuring imagined dialogues between a blind user and a future CAI. Our findings situate CAI use alongside persistent social barriers such as prejudice and restricted employment opportunities that contribute to a lack of social visibility for PVI. These societal conditions shape not only how CAI is used, but also how the potential benefits and limitations of CAI are experienced. We discuss the need for CAI design within the socio-technical realities of PVI, and conclude by discussing the importance of emphasizing social awareness and empowerment in the development of future CAI systems.

Romantic relationships involving ADHD are often challenged when neurocognitive differences are misinterpreted as a lack of care. Most existing tools have focused on correcting the ADHD partner’s behavior, which forces the other into a supervisory role. However, this approach can inadvertently reinforce blame and guilt rather than fostering partnership. In this paper, we investigate how technology can scaffold coordination and mutual understanding in couples including ADHD through interviews with 21 participants and a 7-day diary study with six couples. We found that externalizing expectations and making micro-efforts visible helped partners reframe friction as a coordination challenge rather than a personal failure. While this scaffold lowers the barrier to sensitive communication, it risks becoming a source of daily pressure that couples might eventually try to avoid. We conclude with design implications for neurodiversity-affirming tools that prioritize shared ownership, negotiated norms, and mutual recognition over one-sided correction.

Music education for Deaf and hard-of-hearing (DHH) learners remains challenging because access to auditory cues is constrained and instruction is often shaped by hearing-centered pedagogical and communication structures. Prior work has explored haptics for music, yet offers limited situated knowledge of how haptic cues should be designed and negotiated within authentic instruction. We address this gap through a co-design method centered on reconfigurable haptic prototyping. Using modular tactors that can be rapidly repositioned and retuned, we conducted four co-design workshops with educator–learner dyads in South Korea. The workshops involved hands-on trials, bodystorming, and mock teaching enactment to explore music-to-haptic cues within realistic instructional interaction. We show that haptics can support pedagogical fit, make musical concepts tangible as formative in-the-moment support, and function as a shared reference in instruction. We contribute a co-design method, empirical insights into haptics in music instruction, and design implications for DHH music education.

In China, special education teachers (SETs) for autistic children continuously coordinate complex demands, from intervention planning to behavior interpretation and parent communication. While SETs bear heavy responsibility for these decisions, they often lack the professional feedback loops needed to validate their judgments. Recently, Large Language Model (LLM)–based conversational AI (CAI) has emerged as tools that provide on-demand conversational scaffolding to support teachers’ educational practices. This study examines SETs’ opportunities and challenges in using CAI through a two-week diary study and follow-up interviews with 12 SETs. We found that SETs used CAI as scaffolding tools for interpreting children’s autistic traits, preparing parent communication, and seeking emotional support. Their strategies shifted from task-specific queries to open-ended, experience-driven dialogues that supported reflective sensemaking. However, the need for contextualized guidance often clashed with privacy concerns, making SETs hesitant to share the specific child data required for advice. We conclude with design implications for supporting reflective, responsive, and adaptive trust calibration in the use of CAI.

Team-based collaboration is a cornerstone of modern creative work. Recent advances in generative AI open possibilities for humans to collaborate with multiple AI agents in distinct roles to address complex creative workflows. Yet, how to form Human-Multi-Agent Teams (HMATs) is underexplored, especially given that inter-agent interactions increase complexity and the risk of unexpected behaviors. In this exploratory study, we aim to understand how to form HMATs for creative work using CrafTeam, a technology probe that allows users to form and collaborate with their teams. We conducted a study with 12 design practitioners, in which participants iterated through a three-step cycle: forming HMATs, ideating with their teams, and reflecting on their teams' ideation. Our findings reveal that while participants initially attempted autonomous team operations, they ultimately adopted team formations in which they directly orchestrated agents. We discuss design considerations for HMAT formation that humans can effectively orchestrate multiple agents.

Effective feedback, including critique and evaluation, helps designers develop design concepts and refine their ideas, supporting informed decision-making throughout the iterative design process. However, in studio-based design courses, students often struggle to provide feedback due to a lack of confidence and fear of being judged, which limits their ability to develop essential feedback-giving skills. Recent advances in large language models (LLMs) suggest that role-playing with AI agents can let learners engage in multi-turn feedback without the anxiety of external judgment or the time constraints of real-world settings. Yet prior studies have raised concerns that LLMs struggle to behave like real people in role-play scenarios, diminishing the educational benefits of these interactions. Therefore, designing AI-based agents that effectively support learners in practicing and developing intellectual reasoning skills requires more than merely assigning the target persona’s personality and role to the agent. By addressing these issues, we present Feed-O-Meter, a novel system that employs carefully designed LLM-based agents to create an environment in which students can practice giving design feedback. The system enables users to role-play as mentors, providing feedback to an AI mentee and allowing them to reflect on how that feedback impacts the AI mentee’s idea development process. A user study (N=24) indicated that Feed-O-Meter increased participants’ engagement and motivation through role-switching and helped them adjust feedback to be more comprehensible for an AI mentee. Based on these findings, we discuss future directions for designing systems to foster feedback skills in design education.

Music can play an important role in the lives of some Deaf and Hardof-Hearing (DHH) individuals, facilitating emotional expression, storytelling, and social interaction despite differences in hearing ability and identity.While prior human-computer interaction (HCI) research has introduced various functional advancements to enhance their music experiences, a deeper exploration of broader user experiences and inclusive design strategies remains necessary.To address the real-life challenges DHH individuals face in everyday music activities, we conducted focus group interviews in South Korea with 39 DHH individuals and 9 music experts.Our analysis identified six dimensions of everyday music activities organized by engagement type and social level, highlighting the distinct challenges and preferences DHH individuals encounter in musical contexts.Based on these insights, we propose design implications for fostering more inclusive music experiences, extending beyond individual engagement to include community and mixed-group interactions.This work provides a comprehensive framework to inform future HCI research and guide the development of inclusive technologies that better support DHH individuals' diverse musical experiences.

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With the rise of remote and hybrid work after COVID-19, there is growing interest in understanding remote workers’ experiences and designing digital technology for the future of work within the field of HCI. To gain a holistic understanding of how remote workers navigate the blurred boundary between work and home and how designers can better support their boundary work, we employ humanistic geography as a lens. We engaged in co-speculative design practices with 11 remote workers in the US, exploring how future technologies might sustainably enhance participants’ work and home lives in remote/hybrid arrangements. We present the imagined technologies that resulted from this process, which both reinforce remote workers’ existing boundary work practices through everyday routines/rituals and reclaim the notion of home by fostering independence, joy, and healthy relationships. Our discussions with participants inform implications for designing digital technologies that promote sustainability in the future remote/hybrid work landscape.

Autistic individuals often draw on insights from their supportive networks to develop self-help life strategies ranging from everyday chores to social activities. However, human resources may not always be immediately available. Recently emerging conversational agents (CAs) that leverage large language models (LLMs) have the potential to serve as powerful information-seeking tools, facilitating autistic individuals to tackle daily concerns independently. This study explored the opportunities and challenges of LLM-driven CAs in empowering autistic individuals through focus group interviews and workshops (N=14). We found that autistic individuals expected LLM-driven CAs to offer a non-judgmental space, encouraging them to approach day-to-day issues proactively. However, they raised issues regarding critically digesting the CA responses and disclosing their autistic characteristics. Based on these findings, we propose approaches that place autistic individuals at the center of shaping the meaning and role of LLM-driven CAs in their lives, while preserving their unique needs and characteristics.

Online dating is a space where autistic individuals can find romantic partners with reduced social demands. Autistic individuals are often expected to adapt their behaviors to the social norms underlying the online dating platform to appear as desirable romantic partners. However, given that their autistic traits can lead them to different expectations of dating, it is uncertain whether conforming their behaviors to the norm will guide them to the person they truly want. In this paper, we explored the perceptions and expectations of autistic adults in online dating through interviews and workshops. We found that autistic people desired to know whether they behaved according to the platform’s norms. Still, they expected to keep their unique characteristics rather than unconditionally conform to the norm. We conclude by providing suggestions for designing inclusive online dating experiences that could foster self-guided decisions of autistic users and embrace their unique characteristics.

Today’s youth lives in a world deeply intertwined with AI, which has become an integral part of everyday life. For this reason, it is important for youth to critically think about and examine AI to become responsible users in the future. Although recent attempts have educated youth on AI with focus on delivering critical perspectives within a structured curriculum, opportunities to develop critical thinking competencies that can be reflected in their lives must be provided. With this background, we designed an informal learning experience through an AI-related exhibition to cultivate critical thinking competency. To explore changes before and after the exhibition, 23 participants were invited to experience the exhibition. We found that the exhibition can support the youth in relating AI to their lives through critical thinking processes. Our findings suggest implications for designing learning experiences to foster critical thinking competency for better coexistence with AI.

YouTube is a space where people with disabilities can reach a wider online audience to present what it is like to have disabilities. Thus, it is imperative to understand how content creators with disabilities strategically interact with algorithms to draw viewers around the world. However, considering that the algorithm carries the risk of making less inclusive decisions for users with disabilities, whether the current algorithmic experiences (AXs) on video platforms is inclusive for creators with disabilities is an open question. To address that, we conducted semi-structured interviews with eight YouTubers with disabilities. We found that they aimed to inform the public of diverse representations of disabilities, which led them to work with algorithms by strategically portraying disability identities. However, they were disappointed that the way the algorithms work did not sufficiently support their goals. Based on findings, we suggest implications for designing inclusive AXs that could embrace creators’ subtle needs.

Webtoon is a type of digital comics read online where readers can leave comments to share their thoughts on the story. While it has experienced a surge in popularity internationally, people with visual impairments cannot enjoy webtoon with the lack of an accessible format. While traditional image description practices can be adopted, resulting descriptions cannot preserve webtoons’ unique values such as control over the reading pace and social engagement through comments. To improve the webtoon reading experience for BLV users, we propose Cocomix, an interactive webtoon reader that leverages comments into the design of novel webtoon interactions. Since comments can identify story highlights and provide additional context, we designed a system that provides 1) comments-based adaptive descriptions with selective access to details and 2) panel-anchored comments for easy access to relevant descriptive comments. Our evaluation (N=12) showed that Cocomix users could adapt the description for various needs and better utilize comments.

The number of people with vision impairments using Conversational Agents (CAs) has increased because of the potential of this technology to support them. As many visually impaired people are accustomed to understanding fast speech, most screen readers or voice assistant systems offer speech rate settings. However, current CAs are designed to interact at a human-like speech rate without considering their accessibility. In this study, we tried to understand how people with vision impairments use CA at a fast speech rate. We conducted a 20-day in-home study that examined the CA use of 10 visually impaired people at default and fast speech rates. We investigated the difference in visually impaired people's CA use with different speech rates and their perception toward CA at each rate. Based on these findings, we suggest considerations for the future design of CA speech rate for those with visual impairments.