Designers often engage with video to gain rich, temporal insights about the context of users, collaboratively analyzing it to gather ideas, challenge assumptions, and foster empathy. To capture the full visual context of users and their situations, designers are adopting 360° video, providing richer, more multi-layered insights. Unfortunately, the spherical nature of 360° video means designers cannot create tangible video artifacts such as storyboards for collaborative analysis. To overcome this limitation, we created Tangi, a web-based tool that converts 360° images into tangible 360° video artifacts, that enable designers to embody and share their insights. Our evaluation with nine experienced designers demonstrates that the artifacts Tangi creates enable tangible interactions found in collaborative workshops and introduce two new capabilities: spatial orientation within 360° environments and linking specific details to the broader 360° context. Since Tangi is an open-source tool, designers can immediately leverage 360° video in collaborative workshops.

Retrieval-Augmented Generation (RAG) chatbots show promise in educational settings, yet their application in industrial design, with its iterative and reflective workflows, remains underexplored. This study investigates how master’s students in industrial design perceive the effectiveness of a RAG chatbot in supporting their graduation projects. We developed a chatbot prototype trained on 132 industrial design theses (2021–2023), employing semantic search, multimodal capabilities, and stage-specific guidance, and evaluated it through a mixed-methods approach involving a quantitative question-ranking task (n=7) and a qualitative focus group (n=4). Findings indicate strong performance for practical, early-stage queries but highlight issues with irrelevant corpus results, verbose outputs, and underused features, with five key themes emerging: corpus relevance, output reliability, interaction clarity, multimodal support, and experience-oriented learning. These results inform design guidelines for behaviorally aligned RAG chatbots, enhancing support for critical thinking and process navigation in industrial design education.

Designers can immerse themselves into the world of users by using 360° video leading to richer insights and better solutions. However, 360° video is challenging to share and incompatible with existing tools, preventing designers from effectively integrating it into their iterative and collaborative workflows. To address these challenges, we developed D360, a tool that enables designers to view, annotate, and collaboratively analyze 360° video. D360 features a web-based 360° video viewing and annotation tool, a database, and Miro integration to analyze 360° video using a familiar collaborative process. We evaluated D360 using walk-throughs with six professional designers that verified its utility and identified improvements to creating and presenting annotations. By providing both design directions for future 360° video tools for designers and our open source tool, we enable practitioners and researchers to leverage the rich interaction and visual context of 360° video for more impactful insights.

Ideation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates a state-of-the-art Vision-Language Model (VLM) with a context-enhanced Large Language Model (LLM) to support ideation in VBD. To evaluate DesignMinds, we conducted a between-subject study with 35 design practitioners, comparing its performance to a baseline condition. Our results demonstrate that DesignMinds significantly enhances the flexibility and originality of ideation, while also increasing task engagement. Importantly, the introduction of this technology did not negatively impact user experience, technology acceptance, or usability.

eHealth systems, such as digital care applications or remote monitoring devices, can improve health outcomes using user-centered design principles to create medical devices that adapt to users’ needs and contexts. Data-enabled design (DED) builds on these principles by leveraging user-generated data to iteratively refine systems based on real-world use, enabling adaptive and context-sensitive solutions. However, its exploratory and iterative nature conflicts with the rigid protocols required in clinical trials to evaluate safety and effectiveness. This study revises DED in alignment with clinical trial requirements, identifying four key challenges and proposing a four-phase Clinical Data-Enabled Design (C-DED) framework. This framework reconciles exploratory design with trial methodological demands, supporting the development of safe, effective, and user-centered eHealth medical devices.

Despite advancements in socially aware navigation, robots still often behave inappropriately in social environments. To ensure successful application, robots must detect the human perceived appropriateness of their navigation behaviors. This paper presents a novel dataset covering a complete range of perceived appropriateness and uniquely incorporates human emotion and attention to facilitate the detection of perceived appropriateness of robot social navigation in pathways (PARSNiP). It is created based on a series of human-robot interaction experiments with 30 participants and a mobile robot. Several typical machine learning models are utilized to evaluate the dataset and analyze the contributions of different features in detecting perceived appropriateness. The results indicate that incorporating emotional and attentional features can significantly improve the accuracy of perceived appropriateness detection. There was an increase from 63% to 68% using algorithm-predicted emotional and attentional features, and a further increase to 79% with the emotion and attention data reported by the participants. With the dataset, researchers could train machine learning models to enable robots to detect perceived appropriateness accurately, fostering adaptations that improve their responsiveness and accuracy in social interactions. The dataset is available for download at https://github.com/duibcuiegiosahxois/PARSNiP.git, and videos will be shared upon request by contacting Y.Zhou-13@tudelft.nl.

Data Humanism has gained prominence in personal visualization and Personal Informatics, advocating for a subjective and slow approach to engage with personal data. Collaborative sensemaking has great potential for aiding the understanding of personal data, yet little is known about addressing requirements of structure and coordination when integrating Data Humanism into collaborative visualization. In this paper, we propose design principles for creating both subjective and effective collaborative visualizations, while coordinating the slow sensemaking process and promoting data awareness and communication. We operationalize these principles into a personal visualization toolkit, which we evaluate with an observational study involving 16 university students (8 pairs) analyzing each other's screen-time data. Our findings reveal that implementing the proposed design principles: (1) facilitated data comparison from shared subjective perspectives, (2) helped coordinate sensemaking while allowing time for understanding personal data, and (3) helped the contextualization of data patterns, in turn aiding self-reflection.

The rise of large language models for client-facing conversational search in healthcare necessitates evaluation frameworks that enable the assessment and comparison of these tools. Most such frameworks centre around the automated calculation of performance-related metrics and benchmarks. Though necessary, this focus fails to account for the human factors that impact the development, use, and adoption of these systems, as well as the factors specific to the healthcare context. Human evaluation frameworks attempt to address these drawbacks, but few such frameworks have been developed so far, and even fewer are those based on expert insight. In this work, we conduct semi-structured interviews with eleven healthcare professionals in health lifestyle care. From these interviews, we contribute a two-part healthcare domain expert evaluation framework, (K) Knowledge and (I) Interaction, which organises seven evaluation metrics. Our results reveal key understudied metrics for evaluation like (I1) Context-Seeking, (I2) Empathy, and (I3) Trustworthiness.

Extracting concepts and understanding relationships from videos is essential in Video-Based Design (VBD), where videos serve as a primary medium for exploration but require significant effort in managing meta-information. Mind maps, with their ability to visually organize complex data, offer a promising approach for structuring and analysing video content. Recent advancements in Large Language Models (LLMs) provide new opportunities for meta-information processing and visual understanding in VBD, yet their application remains underexplored. This study recruited 28 VBD practitioners to investigate the use of prompt-tuned LLMs for generating mind maps from ethnographic videos. Comparing LLM-generated mind maps with those created by professional designers, we evaluated rated scores, design effectiveness, and user experience across two contexts. Findings reveal that LLMs effectively capture central concepts but struggle with hierarchical organization and contextual grounding. We discuss trust, customization, and workflow integration as key factors to guide future research on LLM-supported information mapping in VBD.

This paper explores pair collaboration as a novel approach for making sense of personal data. Pair collaboration - characterized by dyadic comparison and structured roles for questioning and reasoning - has proven effective for co-constructing knowledge. However, current collaborative visualization tools primarily focus on group comparisons, overlooking the challenges of accommodating pair collaboration in the context of personal data. To address this gap, we propose a set of design rationales supporting subjective data analysis through dyadic comparison and mixed-focus collaboration styles for co-constructing personal narratives. We operationalize these principles in a tangible visualization toolkit, PAIRcolator. Our user study demonstrates that pairwise collaboration facilitated by the toolkit: 1) reveals detailed data insights that are effective for recalling personal experiences, and 2) fosters a structured, reciprocal sensemaking process for interpreting and reconstructing personal experiences beyond data insights. Our results shed light on the design rationales for, and the processes of pair sensemaking of personal data, and their effects to foster deep levels of reflection.

Data donation is an emerging practice enabling personal data collection for research. While it offers opportunities to access new insights into people’s behavior and experiences through their digital-trace data, the role of individuals – as research participants – is limited in most data donation projects. They primarily contribute with data, limiting the perspectives included and accounted for around critical research-design decisions. In this paper, we explore the opportunity to embed data donation in research processes that are not only contributory but collaborative and co-created. To do so, we present a participatory data donation case study focused on athletes’ perceptions of the impact of their menstrual cycle on their sports performance through their physical activity data. Based on the data donation experiences of 20 athletes, our paper provides insights into people’s preferences and expectations in participatory data donation processes and discusses considerations for supporting various degrees of participation in future data donation research.

Most people interact with digital technologies that collect personal data about their behavior and experiences, leaving behind a data trail. The data within this trail is abstract and difficult to interpret; still, people often need to decide about its collection and distribution. Hence, it is paramount to support personal data literacy, for which data visualization approaches have been successful. These approaches focus mostly on data from single sources (e.g., IoT devices at home) or types (e.g., menstrual logs) and fail to capture people’s situated knowledge. We hypothesize that creating data comics can address these limitations and support people in developing personal data literacy. In this paper, we explore how non-data experts create personal data comics, starting from simple data visualizations, and investigate their effectiveness and engagement in the context of pregnancy. Doing so, we identify comic elements that facilitate the autonomous exploration of personal data and provide design recommendations to support independent data comic creation.

The increased ubiquity of 360° video presents a unique opportunity for designers to deeply engage with the world of users by capturing the complete visual context. However, the opportunities and challenges 360° video introduces for video design ethnography are unclear. This study investigates this gap through 16 workshops in which experienced designers engaged with 360° video. Our analysis shows that while 360° video enhances designers’ ability to explore and understand user contexts, it also complicates the process of sharing insights. To address this challenge, we present two opportunities to support the use of 360° video by designers - the creation of designerly 360° video annotation tools, and 360° “screenshots” - in order to enable designers to leverage the complete context of 360° video for user research.

In social environment navigation, robots inevitably exhibit behaviors that are perceived as inappropriate by humans. Current robots lack the ability to adapt to such human perceptions, leading to repeated inappropriate behaviors. This study employs a mixed-methods approach to explore human-preferred robot adaptations, combining qualitative data from a series of human-robot interactions and a semi-structured interview, and quantitative data from an online survey. 12 participants were recruited to interact with a mobile robot in an indoor setting, reporting 139 instances of inappropriate robot behaviors. The subsequent semi-structured interviews regarding these instances yielded 9 types of inappropriate behaviors and 10 major types of human-preferred robot adaptations, ranging from general ones, such as stopping the motion, to more specific ones, like moving away and then stopping. Additionally, 12 human-preferred adaptations were selected from the interview data and presented to the same participants through an online survey to evaluate their effectiveness in addressing the inappropriate behaviors previously identified. The results reveal the human preference for the robot to move to the side and then stop in most scenarios, which might serve as a general adaptation for addressing inappropriate robot navigation behaviors.

Public sector organizations increasingly use artificial intelligence to augment, support, and automate decision-making. However, such public AI can potentially infringe on citizens’ right to autonomy. Contestability is a system quality that protects against this by ensuring systems are open and responsive to disputes throughout their life cycle. While a growing body of work is investigating contestable AI by design, little of this knowledge has so far been evaluated with practitioners. To make explicit the guiding ideas underpinning contestable AI research, we construct the generative metaphor of the Agonistic Arena, inspired by the political theory of agonistic pluralism. Combining this metaphor and current contestable AI guidelines, we develop an infographic supporting the early-stage concept design of public AI system contestability mechanisms. We evaluate this infographic in five workshops paired with focus groups with a total of 18 practitioners, yielding ten concept designs. Our findings outline the mechanisms for contestability derived from these concept designs. Building on these findings, we subsequently evaluate the efficacy of the Agonistic Arena as a generative metaphor for the design of public AI and identify two competing metaphors at play in this space: the Black Box and the Sovereign.

Ideation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates a state-of-the-art Vision-Language Model (VLM) with a context-enhanced Large Language Model (LLM) to support ideation in VBD. To evaluate DesignMinds, we conducted a between-subject study with 35 design practitioners, comparing its performance to a baseline condition. Our results demonstrate that DesignMinds significantly enhances the flexibility and originality of ideation, while also increasing task engagement. Importantly, the introduction of this technology did not negatively impact user experience, technology acceptance, or usability.

Video-Based Design (VBD) is a design methodology that utilizes video as a primary tool for understanding user interactions, prototyping, and conducting research to enhance the design process. Artificial Intelligence (AI) can be instrumental in video-based design by analyzing and interpreting visual data from videos to enhance user interaction, automate design processes, and improve product functionality. In this study, we explore how AI can enhance professional video-based design with a State-of-the-Art (SOTA) deep learning model. We developed a prototype annotation platform (MarkupLens) and conducted a between-subjects eye-tracking study with 36 designers, annotating videos with three levels of AI assistance. Our findings indicate that MarkupLens improved design annotation quality and productivity. Additionally, it reduced the cognitive load that designers exhibited and enhanced their User Experience (UX). We believe that designer-AI collaboration can greatly enhance the process of eliciting insights in video-based design.

The design of collaborative personal informatics (PI) has shifted its focus from using one’s own data to integrating others’ data to enhance self-understanding. In this trend, understanding the effectiveness of the two data sources in facilitating personal insights becomes essential, as a comprehensive understanding of self-understanding requires insights from both individual and interpersonal perspectives. While recent studies have suggested the potential role of others’ data as a reflective medium to generate personal insights, little is understood about its distinctive effectiveness in personal insights generated compared to one’s own data. To address this gap, we conducted a crowdsourced study involving two participant groups (N1=N2=60) in a data-informed reflection task: Data Providers (DP) reflecting on their own data; Non-Data Providers (NDP) reflecting on the data provided by DP. Analyzing the textual responses, we assess the reflection levels, self-disclosure levels, and characteristics of personal insights. Our findings uncover that others’ data possess a comparable effectiveness in facilitating reflection and self-disclosure of personal thoughts and feelings. Others’ data displays a strength in supporting value judgments, while one’s own data excels in enhancing behavioral awareness. This research sheds light on the design of collaborative PI, offering insights into how to leverage the benefits while mitigating the disadvantages of both data sources to enhance the self-understanding.

eHealth development faces the challenge of generating evidence about health effectiveness in real-world settings. Designers can potentially support this challenge but must understand health approaches to evidence generation about health outcomes. This case study investigates how health and care professionals conceptualise health outcomes and their evidence generation in eHealth. Our results identify three key conceptual dimensions: effect, meaning, and collection. We discuss how these inform future design competencies to support evidence generation about health outcomes in eHealth design.

Data donation is an emerging practice for collecting personal data. However, recent data donation approaches are insufficient in intimate research contexts as they perceive data as neutral and objective and do not consider the contexts where data is generated and shaped nor offer choices beyond whether to disclose data. In this paper, we investigate how Data Feminism can inform an alternative form of data donation and propose the Sensitive Data Donation (sDD) method. It recognizes the sensitive nature of data and assumes the importance of situating and contextualizing it through balanced participation from donors, either as contributors, collaborators, or co-creators. To develop the method, we conduct a scoping literature review where we conceptualize data donation theories and practices. These serve as a base to critique recent approaches and propose an alternative: sDD. It comprises five principles integrated into a five-phase approach. We conclude by discussing its limitations and future challenges.