Playing cards have long been a central feature in board games, despite their physical limitations, such as fragility, and repetitive content. Yet, while HCI research increasingly explores augmented board games, interactive playing cards remain underexplored. In this work, we investigate the use of e-paper displays as interactive playing cards. Based on a formative study with eight game designers, we designed and implemented InkStack—a prototype consisting of e-paper cards, a dedicated programmer, and a web application for card customisation. We then evaluated InkStack in a within-subject study with n = 20 participants, comparing its use across four board game mechanics against traditional paper cards and a smartphone. Results show that InkStack is preferred for more complex mechanics, whereas paper and smartphones are sufficient for simpler tasks. The findings also highlight how customisation and versatility can enhance gameplay and enable novel forms of interaction.

The ambiguity surrounding ‘creepy technology’ in HCI poses significant challenges, as its interpretation varies considerably based on research focus. While it is commonly linked to the appearance of robots and concerns about data privacy and security, it is increasingly associated with functionality, unpredictability, and appropriateness of system behaviour and interaction. Clarifying this ambiguity is vital for designers, as it directly affects user experience and trust in technology. To address this, we undertook a systematic review of 115 papers, identifying key themes related to design choices and contextual elements of creepiness. Based on this review, we developed the Creepiness Framework, which comprises two main components: the Structure of Creepiness and Felt Creepiness. The framework clarifies the dynamics of creepy technology. Additionally, we derive design-relevant insights into creepiness features that can guide the development of technologies that are more predictable and less likely to evoke unintended feelings of creepiness.

Most studies of Personal Informatics (PI) focus on the holistic experience of self-tracking or how users relate to self-tracking goals. Recently, new tracker metrics became available in commercial systems, e.g. stress scores or body battery. Hence, more attention should be devoted to what users track and how they understand metrics produced by their trackers. Charting the evolution of metrics in PI can enable building systems that better support well-being. To this end, we interviewed n = 25 fitness tracker users to discover what metrics are most important to them, how they understand the metrics, and how they formulate their goals with respect to the metrics. We found that users created a metric ecology which they adjusted to their life circumstances, reformulating their goals. We identified key issues in understanding metrics which bear the risk of misuse. We contribute recommendations for future PI systems as self-tracking metrics increase in complexity.

While Personal Informatics (PI) tools utilise data visualisations to communicate behaviour, users often struggle to make sense of their data and translate it to actionable insights. Conversational Agents (CAs) offer potential for improved access to PI data, yet their role in PI tools remains under-explored. We conducted a two-week user study with journals, interviews and logging with n = 36 participants using a novel commercial fitness tracker with an embedded CA. We identified the give and take principle as essential for meaningful sensemaking with a CA—a dynamic resulting in more effective interactions given users’ inputs ( give ) are met with prompts that are sufficiently specific and built upon prior data engagement ( take ). A critical point was how users perceived the CA during their initial interactions, with first impressions often determining further engagement. We contribute insights into how CAs can support or hinder the PI experience, offering implications for future PI system designs.

Decision-making aided by Artificial Intelligence in high-stakes domains such as law enforcement must be informed and accountable. Thus, designing explainable artificial intelligence (XAI) for such settings is a key social concern. Yet, explanations are often misunderstood by end-users due to being overly technical or abstract. To address this, our study engaged with police employees in the Netherlands, who are users of a text classifier. We found that for them, usability and usefulness are of great importance in explanation design, whereas interpretability and understandability are less valued. Further, our work reports on how design elements included in machine learning model explanations are interpreted. Drawing from these insights, we contribute recommendations that guide XAI system designers to cater to the specific needs of specialized users in high-stakes domains and suggest design considerations for machine learning model explanations aimed at domain experts.

Dedicated handheld controllers facilitate haptic experiences of virtual objects in mixed reality (MR). However, as mobile MR becomes more prevalent, we observe the emergence of controller-free MR interactions. To retain immersive haptic experiences, we explore the use of mobile devices as a substitute for specialised MR controller. In an exploratory gesture elicitation study (n = 18), we examined users’ (1) intuitive hand gestures performed with prospective mobile devices and (2) preferences for real-time haptic feedback when exploring haptic object properties. Our results reveal three haptic exploration modes for the mobile device, as an object, hand substitute, or as an additional tool, and emphasise the benefits of incorporating the device’s unique physical features into the object interaction. This work expands the design possibilities using mobile devices for tangible object interaction, guiding the future design of mobile devices for haptic MR experiences.