Smart home applications aim to increase convenience, yet often require authentication to protect sensitive data. This is non-trivial: effortful authentication contradicts intended convenience, the multitude of devices raises scalability issues, many devices lack suitable interfaces, and the presence of other inhabitants requires intentional and acceptable interactions. To address these issues, we explored new and creative authentication interactions with an interaction relabeling approach using everyday objects. We conducted six focus group workshops with 20 participants in a living room and a kitchen setting that resulted in a variety of creative authentication interactions with analogue and digital objects. Furthermore, participants created authentication interactions based on tasks that they have to or wish to perform anyway such as cleaning the kitchen—thus primary tasks. This led us to explore the option to transform authentication from being an additional, secondary task toward using primary tasks further in an online study with 194 participants. Relevant implications in terms of acceptable authentication task characteristics, user perceptions, arising security challenges, and psychological habit research are discussed.

Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images to reduce risks of sub-optimally or critically placed NGTs being missed or delayed in their detection, but gaps remain in clinical practice integration. In this study, we present a human-centered approach to the problem and describe insights derived following contextual inquiry and in-depth interviews with 15 clinical stakeholders. The interviews helped understand challenges in existing workflows, and how best to align technical capabilities with user needs and expectations. We discovered the trade-offs and complexities that need consideration when choosing suitable workflow stages, target users, and design configurations for different AI proposals. We explored how to balance AI benefits and risks for healthcare staff and patients within broader organizational, technical, and medical-legal constraints. We also identified data issues related to edge cases and data biases that affect model training and evaluation; how data documentation practices influence data preparation and labelling; and how to measure relevant AI outcomes reliably in future evaluations. We discuss how our work informs design and development of AI applications that are clinically useful, ethical, and acceptable in real-world healthcare services.

With the increase of Internet of Things devices in home environments, data will become an even more dominant part of people’s everyday lives. The invisibility of data leads us to rely on our imagination to make sense of them, yet this imagination is heavily shaped by a technocentric lens that views data as neutral and transparent. In response, in this article, we present the Data Epics project, where we commissioned seven fiction writers to write short stories based on smart home device data provided by seven households. We offer an analysis of the writers and households’ experiences with the project, presenting seven ways in which data imaginaries are made and unmade. We contribute a reflection around how making new data imaginaries unmakes common ones, the friction in unmaking certain imaginaries, and how we might further disseminate alternative data imaginaries.

While chatbots are increasingly used for customer service, there is a knowledge gap concerning the impact of Conversational Breakdown in such chatbot interactions. In a 2 \({\times}\) 4 factorial design online experiment, we studied how Conversational Breakdown impacts user emotion and trust in a chatbot for customer service, given variations in task criticality and breakdown task order. Here, 257 participants were randomly assigned to complete high- or low-criticality tasks with a prototype chatbot for customer service, experiencing Conversational Breakdown for the first, second, third or none of their tasks. The task set was decided from a 63-participant pre-study. We found significant impact of Conversational Breakdown, including a marked order effect on overall trust, as well as a bounce-back effect on task-specific trust and emotion after subsequent successful task completion. We found no post-interaction effect of Task Criticality. Based on our findings, we discuss theoretical and practical implications and suggest future research.

Internet users often neglect important security actions (e.g., installing security updates or changing passwords) because they interrupt users’ main task at inopportune times. Commitment devices, such as reminders and promises, have been found to be effective at reducing procrastination in other domains. In a series of online experiments ( \(n{\gt}3{,}000\) ), we explored the effects of reminders and promises on users’ willingness to change a compromised password. We find that adding an option to delay the task increases the share of people willing to eventually change their password considerably. Critically, the option to delay yields this overall increase without reducing the share of people choosing to change their password immediately. Additionally, most participants who promised to change their password later, or asked to be reminded to do so, indeed followed through on their commitment, leading to a net positive effect. Reminding participants of their previous commitment further increased this effect.

Social anxiety is a prevalent mental health concern that impacts quality of life and makes social spaces less accessible. We conducted two studies with socially anxious participants, investigating using affective haptic comfort objects to provide calming support during social exposure. Participatory prototyping informed the design and use of the intervention, which was then evaluated between-groups with a social exposure task. Treatment participants held their preferred vibration-augmented prototype during this task; control participants did not. We observed no change in physiological measures, but treatment participants exhibited a significantly broader distribution of psychological anxiety scores. Participants in both studies found their objects pleasant and calming, made positive emotional associations with resonant stimuli, and used their objects to afford self-soothing tactile experiences. We discuss how future designers can facilitate calming affective haptic interfaces for socially anxious settings.

Individuals are known to differ in cognitive abilities, affecting their behavior and information processing in digital environments. However, we have a limited understanding of which behaviors are affected, how, and whether some features extracted from digital behavior can predict cognitive abilities. Consequently, researchers may miss opportunities to design and support individuals with personalized experiences and detect those who may benefit from additional interventions. To characterize digital behaviors, we collected 24/7 screen recordings, input behavior, and operating system data from the laptops of 20 adults for two weeks. We use cognitive test results from the same individuals to characterize their cognitive abilities: psychomotor speed, processing speed, selective attention, working memory, and fluid intelligence. Our results from regression analysis, path modeling, and machine learning experiments show that cognitive abilities are associated with differences in digital behavior and that naturalistic behavioral data can predict the cognitive abilities of individuals with small error rates. Our findings suggest naturalistic interaction data as a novel source for modeling cognitive differences.

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show anAI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact theAI Ghostwriter Effect, and higher levels of participants’ influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.

Non-contact, mid-air haptic devices have been utilized for a wide variety of experiences, including those in extended reality, public displays, medical, and automotive domains. In this work, we explore the use of synthetic jets as a promising and under-explored mid-air haptic feedback method. We show how synthetic jets can scale from compact, low-powered devices, all the way to large, long-range, and steerable devices (Figure 1 ). We built seven functional prototypes targeting different application domains to illustrate the broad applicability of our approach. These example devices are capable of rendering complex haptic effects, varying in both time and space. We quantify the physical performance of our designs using spatial pressure and wind flow measurements and validate their compelling effect on users with stimuli recognition and qualitative studies.

Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likely to have profound effects in the classroom, where novices learning to code can now use free tools to automatically suggest solutions to programming exercises and assignments. However, little is currently known about how novices interact with these tools in practice. We present the first study that observes students at the introductory level using one such code auto-generating tool, Github Copilot, on a typical introductory programming (CS1) assignment. Through observations and interviews we explore student perceptions of the benefits and pitfalls of this technology for learning, present new observed interaction patterns, and discuss cognitive and metacognitive difficulties faced by students. We consider design implications of these findings, specifically in terms of how tools like Copilot can better support and scaffold the novice programming experience.