This paper reconsiders knowledge sharing as a relational process of disorientation and reorientation, which is a way of turning toward others, the world, and possible futures. Drawing on an intergenerational participatory speculative design project in a historically Black neighborhood, we show how seniors and youth shared knowledge of turning toward the affective, more-than-human relations, as well as toward one another and collective futures. These practices reoriented participants across differences toward one another and their community, cultivating new cross-generational relationships and shared imagination. Building on Sara Ahmed's notion of orientation and Black feminist thought, we argue that knowledge sharing is less about efficiency or productivity and more about relational alignment---negotiating how communities come together and move together toward otherwise futures. For HCI, this critical reframing foregrounds the racialized histories and everyday labors that sustain collective orientations, urging scholars and designers to consider how communities can collectively (re)orient themselves epistemically and ontologically through design, and what otherwise worlds those reorientations make possible.
Conversational agents are increasingly used in education for learning support. An application is "learning by explaining", where learners explain their understanding to an agent. However, existing research focuses on single roles, leaving it unclear how different pedagogical roles influence learners' interaction patterns, learning outcomes and experiences. We conducted a between-subjects study (N=96) comparing agents with three pedagogical roles (Tutee, Peer, Challenger) and a control condition while learning an economics concept. We found that different pedagogical roles shaped learning dynamics, including interaction patterns and experiences. Specifically, the Tutee agent elicited the most cognitive investment but led to high pressure. The Peer agent fostered high absorption and interest through collaborative dialogue. The Challenger agent promoted cognitive and metacognitive acts, enhancing critical thinking with moderate pressure. The findings highlight how agent roles shape different learning dynamics, guiding the design of educational agents tailored to specific pedagogical goals and learning phases.
As generative AI (GenAI) is increasingly applied in persona development to represent real users, understanding the implications and limitations of this technology is essential for establishing robust practices. This scoping review analyzes how 81 articles (2022-2025) use GenAI techniques for the creation, evaluation, and application of personas. The articles exhibited good level of reproducibility, with 61% of articles sharing resources (personas, code, or datasets). Furthermore, conversational persona interfaces are increasingly provided alongside traditional profiles. However, nearly half (45%) of the articles lack evaluation, and the majority (86%) use only GPT models. In some articles, GenAI use creates a risk of circularity, in which the same GenAI model both generates and evaluates outputs. Our findings also suggest that GenAI seems to reduce the role of human developers in the persona-creation process. To mitigate the associated risks, we propose actionable guidelines for the responsible integration of GenAI into persona development.
Metaphors enable designers to communicate their ideal user experience for platforms. Yet, we often do not know if these design metaphors match users’ actual experiences. In this work, we compare design and user metaphors across three different platforms: ChatGPT, Twitter, and YouTube. We build on prior methods to elicit 554 user metaphors, as well as ratings on how well each metaphor describes users’ experiences. We then identify 21 design metaphors by analyzing each platform’s historical web presence since their launch date. We find that design metaphors often do not match the metaphors that users use to describe their experiences. Even when design and user metaphors do match, the metaphors do not always resonate universally. Through these findings, we highlight how comparing design and user metaphors can help to evaluate and refine metaphors for user experience.
In 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.
While HCI research acknowledges that prior knowledge can be transferred from familiar interfaces to unfamiliar ones, we lack an understanding of which interface characteristics support this process. To adress this issue, we conducted three experiments to identify the interface characteristics that influence the perception of \dis between software interfaces. The first, which involves a card-sorting activity, identifies seven intrinsic characteristics of interface. The second, conducted via an online pairwise comparison survey, identifies three characteristics inherent to the interface's display context. Finally, the third experiment contrasts the ten identified characteristics to determine their respective influence on perceived interface \dis. Altogether, our results provide actionable guidance for understanding how users perceive differences between interfaces and how such perceptions may inform or facilitate analogical transfer of knowledge from familiar to unfamiliar interfaces.
The internet is becoming increasingly visual, but social computing research and methodological training has relied heavily on textual methods. Methodological innovation is needed to study visual social data, including problematic information (mis- and disinformation, propaganda, hate, AI slop, etc). Contending with this, we present a framework for conducting grounded, interpretive, computationally supported, mixed-method research on collections of visual social media data. We developed this framework while grappling with the ethical, logistical, and methodological challenges of conducting in-depth analysis of potentially harmful visual content while caring for our research team. We document our framework components of visual grammars, human analysis, and computationally supported analysis with an umbrella commitment to care and its use in three empirical case studies. We also provide recommendations and implications for the HCI community in embracing training in and the advancing of visual methods and research, including a sensitizing concept of visual integrity.