Labels inform smart home users about the privacy of devices before purchase and during use. Yet, current privacy labels fail to fully reflect the impact of advanced device configuration options like sensor state control. Based on the successful implementation of related privacy and security labels, we designed extended static and interactive labels that reflect sensor states and device connectivity. We first did expert interviews (N = 10) that informed the final label design. Second, we ran an online survey (N = 160) to assess the interpretation and usability of the novel interactive privacy label. Lastly, we conducted a second survey (N = 120) to investigate how well our interactive labels educate users about sensor configuration. We found that most participants successfully used the interactive label and retrieved sensor information more efficiently and correctly. We discuss our findings in the context of a potential shift in label use toward control and use-case-based interaction.

AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.

Allowing users of interactive systems to reflect on their task proficiency is often incidental. This is unfortunate, as communicating meaningful task-related proficiency feedback could improve users’ awareness of their abilities and their willingness to improve. To highlight the feasibility of this concept, we evaluated how different methods of readability feedback impacted users during a text production task. In general, our results showed that having access to readability feedback allowed participants to reflect on their task solving approach, facilitating the users’ understanding of their proficiency. Revision-based methods are less distracting for the user than continuous feedback methods, while still offering high efficacy. Further, feedback should be paired with a subtle form of gamification elements. We envision this reflection-oriented design to user proficiency to be applicable to a variety of interactive systems, allowing for an improved and engaging user experience.

Most smart home devices have multiple sensors, such as cameras and microphones; however, most cannot be controlled individually. Tangible privacy mechanisms provide control over individual sensors and instill high certainty of privacy. Yet, it remains unclear how they can be used in future smart homes. We conducted three studies to understand how tangible privacy mechanisms scale across multiple devices and respond to user needs. First, we conducted a focus group (N=8) on speculative tangible control artifacts to understand the user perspective. Second, we ran a workshop at a human-computer interaction conference (N=8) on tangible privacy. Third, we conducted a six-week in-the-wild study with a tangible, static privacy dashboard across six households. Our findings help to contrast the need for tangible privacy mechanisms on the sensor level with user needs on a smart home level. Finally, we discuss our design implications for future smart homes through the lens of inclusive privacy.

Pilots in non-commercial aviation have minimal access to digital support tools. Common 2D maps, displayed on tablets, are often the only digital information source that fails to adequately capture the 3D airspace and its surroundings, challenging the pilot’s workload and awareness. In this work, we developed and tested a Mixed Reality (MR) prototype with twelve General Aviation (GA) pilots using a full-sized flight simulator environment. The prototype’s demonstration showcased the capabilities of contemporary technology and its potential applications. Following the simulation, in-depth interviews were conducted with the participating pilots to discern their perspectives on integrating MR solutions into cockpit environments. The study revealed valuable insights into pilots’ concerns, design prerequisites for future systems, and potential use cases. This work not only highlights the feasibility of MR implementations but also provides a foundation for the development of enhanced digital tools for GA, aiming to alleviate pilot workload and augment situational awareness.

Smart home devices with their sophisticated sensing technologies raise many privacy concerns. In most cases, they only function when fully connected to the internet in which case privacy exposure is greatest. Users currently have to either accept these privacy risks or remove devices from the internet or power plug, rendering them useless. Our demo is based on recent work advocating for advanced smart device configuration options across a spectrum of connectivity control options. We introduce a model ecosystem with four connectivity levels and a privacy label that informs about connectivity-feature trade-offs across those four modes. The presented ecosystem introduces two functional smart devices and demonstrates intuitively how configuration decisions impact information flow.

The parallel improvements in multi-material 3D printers and the quality of conductive filament open new possibilities for the fabrication of tangible and functional objects. In this studio, we discuss best practices for 3D printed electronics, talk about encountered problems, and derive design recommendations. We will guide the participants through a fabrication process by practically designing and printing objects. Consequently, we contemplate individual functional fabricated components, including small printed circuits and multi-material prints. We aim to spark a discussion about individually experienced challenges participants encountered during their design and fabrication process. This discussion includes problem-solving strategies, whose insights benefit other participants. Finally, we show the potential of printed electronics and discuss encouraging new opportunities in this field.

This paper presents a systematic review of HCI literature focusing on children with ADHD, the prevailing mental health diagnosis in children. Its aim is to (i) chart the state-of-the-art in this domain (e.g. methods used), (ii) identify the ways the HCI community has addressed the needs of children with ADHD (e.g. technologies deployed), and (iii) describe the involvement of the various stakeholders playing a role in their everyday experiences (i.e. their care ecosystem). Our findings show limited engagement of the care ecosystem in the design, development and user studies of current technologies, and shortcomings in designing for multiple ecosystem stakeholders, despite their crucial role. We also find that most HCI contributions are systems aiming to address ADHD-related symptoms. Based on our findings, we provide suggestions for further research and design considerations for future systems that empower and promote the well-being of children with ADHD, while considering their care ecosystem.

While systems that use Artificial Intelligence (AI) are increasingly becoming part of everyday technology use, we do not fully understand how AI changes design processes. A structured understanding of how designers work with AI is needed to improve the design process and educate future designers. To that end, we conducted interviews with designers who participated in projects which used AI. While past work focused on AI systems created by experienced designers, we focus on the perspectives of a diverse sample of interaction designers. Our results show that the design process of an interactive system is affected when AI is integrated and that design teams adapt their processes to accommodate AI. Based on our data, we contribute four approaches adopted by interaction designers working with AI: a priori, post-hoc, model-centric, and competence-centric. Our work contributes a pragmatic account of how design processes for AI systems are enacted.

Users avoid engaging with privacy policies because they are lengthy and complex, making it challenging to retrieve relevant information. In response, research proposed contextual privacy policies (CPPs) that embed relevant privacy information directly into their affiliated contexts. To date, CPPs are limited to concept showcases. This work evolves CPPs into a production tool that automatically extracts and displays concise policy information. We first evaluated the technical functionality on the US’s 500 most visited websites with 59 participants. Based on our results, we further revised the tool to deploy it in the wild with 11 participants over ten days. We found that our tool is effective at embedding CPP information on websites. Moreover, we found that the tool’s usage led to more reflective privacy behavior, making CPPs powerful in helping users understand the consequences of their online activities. We contribute design implications around CPP presentation to inform future systems design.

Scientists largely acknowledge the value of research data management (RDM) to enable reproducibility and reuse. But, RDM practices are not sufficiently rewarded within the traditional academic reputation economy. Recent work showed that emerging RDM tools can offer new incentives and rewards. But, the design of such platforms and scientists' commitment to RDM is contingent on additional factors, including policies, training, and several types of personal motivation. To date, studies focused on investigating single or few of those RDM components within a given environment. In contrast, we conducted three studies within a global agricultural science organization, to provide a more complete account of RDM commitment drivers: one survey study (n = 23) and two qualitative explorations of regulatory frameworks (n = 17), as well as motivation, infrastructure, and training components (n = 13). Based on the sum of findings, we contribute to the triangulation of a recent RDM commitment evolution model. In particular, we find that strong support and suitable tools help develop RDM commitment, while policy conflicts, unclear data standards, and multi-platform sharing, lead to unexpected negotiation processes. We expect that these findings will help to better understand RDM commitment drivers, refine the RDM commitment evolution model, and benefit its application in science.

Exploring and interacting with electronics is challenging as the internal processes of components are not visible. Further barriers to engagement with electronics include fear of injury and hardware damage. In response, Augmented Reality (AR) applications address those challenges to make internal processes and the functionality of circuits visible. However, current apps are either limited to abstract low-fidelity applications or entirely virtual environments. We present ElectronicsAR, a tangible high-fidelity AR electronics kit with scaled hardware components representing the shape of real electronics. Our evaluation with 24 participants showed that users were more efficient and more effective at naming components, as well as building and debugging circuits. We discuss our findings in the context of ElectronicsAR's unique characteristics that we contrast with related work. Based on this, we discuss opportunities for future research to design functional mobile AR applications that meet the needs of beginners and experts.

Private homes are increasingly becoming smart spaces. While smart homes promise comfort, they expose most intimate spaces to security and privacy risks. Unfortunately, most users today are not equipped with the right tools to assess the vulnerabilities or privacy practices of smart devices. Further, users might lose track of the devices installed in their homes or are unaware of devices placed by a partner or host. We developed SaferHome, an interactive digital-physical privacy framework, to provide smart home users with security and privacy assessments and a sense of device location. SaferHome includes a digital list view and physical and digital dashboards that map real floor plans. We evaluated SaferHome with eight households in the wild. We find that users adopted various strategies to integrate the dashboards into their understanding and interpretation of smart home privacy. We present implications for the design of future smart home privacy frameworks that are impacted by technical affinity, device types, device ownership, and tangibility of assessments.

Science faces a reproducibility crisis. There is a need to establish open science practices within the academic reputation economy. Open Science Badges address this issue by promoting and acknowledging research sharing and documentation. The generic design of currently awarded badges enabled their adoption across the sciences. Yet, their general nature makes it difficult to reflect individual practices and needs of distinct scientific fields. In this paper, we explore uses and effects of highly tailored badges in research data management. We implemented six science badges in a particle physics research preservation service. Our exploration showed that scientists were open to encouraging valuable scientific practices through tailored science badges. They described entirely new opportunities for interaction with research repositories. We present design implications for systems that promote reproducibility, related to meaningful criteria, repository navigation, and content discovery. Finally, we discuss the scope and uses of tailored science badges in modern science.

As science becomes increasingly data-intensive, the requirements for comprehensive Research Data Management (RDM) grow. This often overwhelms scientists, requiring more workload and training. The failure to conduct effective RDM leads to producing research artefacts that cannot be reproduced or reused. Past research placed high value on supporting data science workers, but focused mainly on data production, collection, processing, and sensemaking. In order to understand practices and needs of data science workers in relation to documentation, preservation, sharing, and reuse, we conducted a cross-domain study with 15 scientists and data managers from diverse scientific domains. We identified five core concepts which describe requirements, drivers, and boundaries in the development of commitment for RDM, essential for generating reproducible research artefacts: Practice, Adoption, Barriers, Education, and Impact. Based on those concepts, we introduce a stage-based model of personal RDM commitment evolution. The model can be used to drive the design of future systems that support a transition to open science. We discuss infrastructure, policies, and motivations involved at the stages and transitions in the model. Our work supports designers in understanding the constraints and challenges involved in designing for reproducibility in an age of data-driven science.

Reproducibility should be a cornerstone of scientific research and is a growing concern among the scientific community and the public. Understanding how to design services and tools that support documentation, preservation and sharing is required to maximize the positive impact of scientific research. We conducted a study of user attitudes towards systems that support data preservation in High Energy Physics, one of science's most data-intensive branches. We report on our interview study with 12 experimental physicists, studying requirements and opportunities in designing for research preservation and reproducibility. Our findings suggest that we need to design for motivation and benefits in order to stimulate contributions and to address the observed scalability challenge. Therefore, researchers' attitudes towards communication, uncertainty, collaboration and automation need to be reflected in design. Based on our findings, we present a systematic view of user needs and constraints that define the design space of systems supporting reproducible practices.

The need for data preservation and reproducible research is widely recognized in the scientific community. Yet, researchers often struggle to find the motivation to contribute to data repositories and to use tools that foster reproducibility. In this paper, we explore possible uses of gamification to support reproducible practices in High Energy Physics. To understand how gamification can be effective in research tools, we participated in a workshop and performed interviews with data analysts. We then designed two interactive prototypes of a research preservation service that use contrasting gamification strategies. The evaluation of the prototypes showed that gamification needs to address core scientific challenges, in particular the fair reflection of quality and individual contribution. Through thematic analysis, we identified four themes which describe perceptions and requirements of gamification in research: Contribution, Metrics, Applications and Scientific practice. Based on these, we discuss design implications for gamification in science.