Mobile user interfaces abundantly feature so-called ‘dark patterns’. These deceptive design practices manipulate users’ decision making to profit online service providers. While past research on dark patterns mainly focus on visual design, other sensory modalities such as audio and touch remain largely unexplored. In this early work, we investigate the manipulative side of haptics, which we term as ‘Dark Haptics’, as a strategy to manipulate users. We designed a study to empirically showcase the potential of using a dark haptic pattern in a mobile device to manipulate user actions in a survey. Our findings indicate that our dark haptic design successfully influenced participants to forego their privacy after experiencing an alarming feedback for rejecting intrusive requests in the survey. As a first exploration of manipulative qualities of dark haptic designs, we attempt to lay the groundwork for future research and tools to mitigate harms and risks of dark haptics.

In human communication, responding to a question very slowly or quickly influences our trust in the answer. As chatbots evolve to increasingly mimic human speech, response speed can be artificially varied to create certain impressions on users. However, studies remain inconclusive, potentially due to the absence of contextual cues that allow for interpretation of the delay. Thus, this study explores textual explanations that justify the instant and dynamic – dependent on answer length – response delays. We derive five design variations based on prior work and evaluate their impact on the chatbot’s perceived social presence and transparency (N = 10). In a between-subject online study (N = 194), we then evaluate the influence of the highest-rated justification on users’ perceptions of chatbot transparency, social presence, and trust for the two delay conditions. Results demonstrate that while such justifications enhance perceived transparency and trust in the immediate response scenario, they show no effect in the dynamic delay context.

While Human-Computer Interaction (HCI) has contributed to demonstrating that physiological measures can be used to detect cognitive changes, engineering and machine learning will bring these to application in consumer wearable technology. For HCI, many open questions remain, such as: What happens when this becomes a cognitive form of personal informatics? What goals do we have for our daily cognitive activity? How should such a complex concept be conveyed to users to be useful in their everyday lives? How can we mitigate potential ethical concerns? This is different to designing BCI interactions; we are concerned with understanding how people will live with consumer neurotechnology. This workshop will directly address the future of Cognitive Personal Informatics (CPI), by bringing together design, BCI and physiological data, ethics, and personal informatics researchers to discuss and set the research agenda in this inevitable future.

When booking accommodations such as hotel rooms or vacation houses online, users heavily rely on the experiences of prior customers through reviews. However, such reviews are often short or lacking important details since most UIs limit users to text-only input, making review creation laborious and time-consuming. The quality of the review can therefore vary, depending on the skill and intrinsic motivation of the reviewer. Instead of relying on these two variables, we explore the effects of a tag enriched UI, both for creating and presenting review information. In the process, we evaluate tags – short descriptive text snippets such as “centrally located” – as additional input components to open text fields and rating scales. These tags aim to trigger users’ memory of details and experiences and support them in creating comprehensive reviews without having to generate long texts. In an initial user study, we asked participants to generate reviews with and without the tag support. In a second user study, they evaluated reviews created with and without tags. Our results show that tags were perceived as helpful in creating reviews and increased the level of detail in the experiences reported. We discuss the implications of review quality, helpfulness, and potential limitations.

People are increasingly eager to know more about themselves through technology. To date, technology has primarily provided information on our physiology. Yet, with advances in wearable technology and artificial intelligence, the current advent of consumer neurotechnology will enable users to measure their cognitive activity. We see an opportunity for research in Human-Computer Interaction (HCI) in the development of these devices. Neurotechnology offers new insights into user experiences and facilitates the development of novel methods in HCI. Researchers will be able to create innovative interactive systems based on the ability to measure cognitive activity at scale in real-world settings. In this paper, we contribute a vision of how neurotechnology will transform HCI research and practice. We discuss how neurotechnology prompts a discussion about ethics, privacy, and trust. This trend highlights HCI’s crucial role in ensuring that neurotechnology is developed and utilised in ways that truly benefit people.

Television has long since been a uni-directional medium. However, when TV is used for educational purposes, like in edutainment shows, interactivity could enhance the learning benefit for the viewer. In recent years, AR has been increasingly explored in HCI research to enable interaction among viewers as well as viewers and hosts. Yet, how to implement this collaborative AR (CoAR) experience remains an open research question. This paper explores four approaches to asynchronous collaboration based on the Cognitive Apprenticeship Model: scaffolding, coaching, modeling, and collaborating. We developed a pilot show for a fictional edutainment series and evaluated the concept with two TV experts. In a wizard-of-oz study, we test our AR prototype with eight users and evaluate the perception of the four collaboration styles. The AR-enhanced edutainment concept was well-received by the participants, and the coaching collaboration style was perceived as favorable and could possibly be combined with the modeling style.

Attention management systems seek to minimize disruption by intelligently timing interruptions and helping users navigate multiple tasks and activities. While there is a solid theoretical basis and rich history in HCI research for attention management, little progress has been made regarding their practical implementation and deployment. Building sophisticated attention management systems requires a great variety of sensors, task- and user models, and multiple devices while considering the complexity of user context and human behavior. Novel AI technologies, such as generative systems, reinforcement learning, and large language models, open new possibilities to create intelligent, practical, and user-centered attention management systems. This proposed workshop aims to bring together researchers and practitioners from diverse backgrounds to discuss and formulate a research agenda to advance attention management systems using novel AI tools to manage and mitigate interruptions from computing systems effectively.

While Human-Computer Interaction (HCI) has contributed to demonstrating that physiological measures can be used to detect cognitive changes, engineering and machine learning will bring these to application in consumer wearable technology. For HCI, many open questions remain, such as: What happens when this becomes a cognitive form of personal informatics What goals do we have for our daily cognitive activity How should such a complex concept be conveyed to users to be useful in their everyday life How can we mitigate potential ethical concerns These issues are different from physiologically controlled interactions, such as BCIs, to a time when we have new data about ourselves. This workshop will be the first to directly address the future of Cognitive Personal Informatics (CPI), by bringing together design, BCI and physiological data, ethics, and personal informatics researchers to discuss and set the research agenda in this inevitable future before it arrives.

Virtual Reality (VR) technology provides the elderly, and people with dementia, the opportunity to reminisce by exploring places outside their (care) home, free from age-related (physical) restrictions. However, the elderly are particularly vulnerable to overstimulation. Irresponsible VR design can cause stress and anxiety, potentially even exacerbating cognitive decline, and diminishing well-being. We present an electromyography (EMG) driven stress- and emotion-adaptive VR environment for the elderly that provides an immersive but controlled experience targeted at preventing negative emotions. We report our results and insights from a pilot study with elderly participants (N=3). Our system detects and mitigates signs of stress and negative emotions while promoting pleasant recollections.

Frequent repetition of vocabulary is essential for effective language learning. To increase exposure to learning content, this work explores the integration of vocabulary tasks into the smartphone authentication process. We present the design and initial user experience evaluation of twelve prototypes, which explored three learning tasks and four common authentication types. In a three-week within-subject field study, we compared the most promising concept as mobile language learning (MLL) applications to two baselines: We designed a novel (1) UnlockApp that presents a vocabulary task with each authentication event, nudging users towards short frequent learning session. We compare it with a (2) NotificationApp that displays vocabulary tasks in a push notification in the status bar, which is always visible but learning needs to be user-initiated, and a (3) StandardApp that requires users to start in-app learning actively. Our study is the first to directly compare these embedding concepts for MLL, showing that integrating vocabulary learning into everyday smartphone interactions via UnlockApp and NotificationApp increases the number of answers. However, users show individual subjective preferences. Based on our results, we discuss the trade-off between higher content exposure and disturbance, and the related challenges and opportunities of embedding learning seamlessly into everyday mobile interactions.

The ubiquity of mobile devices in peoples’ everyday life makes them a feasible tool for language learning. Learning anytime and anywhere creates great flexibility but comes with the inherent risk of infrequent learning and learning in interruption-prone environments. No matter the length of the learning break, it can negatively affect knowledge consolidation and recall. This work presents the design and implementation of memory cues to support task resumption in mobile language learning applications and two evaluations to assess their impact on user experience. An initial laboratory experiment (N = 15) revealed that while the presentation of the cues had no significant effect on objective performance measures (task completion time and error rate), the users still perceived the cues as helpful and would appreciate them in a mobile learning app. A follow-up study (N = 16) investigated revised cue designs in a real-world field setting and found that users particularly appreciated our interactive test cue design. We discuss strengths and limitations of our concept and implications for the application of task resumption cues beyond the scope of mobile language learning.

Around 466 million people in the world live with hearing loss, with many benefiting from sign language as a mean of communication. Through advancements in technology-supported learning, autodidactic acquisition of sign languages, e.g., American Sign Language (ASL), has become possible. However, little is known about the best practices for teaching signs using technology. This work investigates the use of different conditions for teaching ASL signs: audio, visual, electrical muscle stimulation (EMS), and visual combined with EMS. In a user study, we compare participants' accuracy in executing signs, recall ability after a two-week break, and user experience. Our results show that the conditions involving EMS resulted in the best overall user experience. Moreover, ten ASL experts rated the signs performed with visual and EMS combined highest. We conclude our work with the potentials and drawbacks of each condition and present implications that will benefit the design of future learning systems.

We present a three-week within-subject field study comparing three mobile language learning (MLL) applications with varying levels of integration into everyday smartphone interactions: We designed a novel (1) UnlockApp that presents a vocabulary task with each authentication event, nudging users towards short frequent learning session. We compare it with a (2) NotificationApp that displays vocabulary tasks in a push notification in the status bar, which is always visible but learning needs to be user-initiated, and a (3) StandardApp that requires users to start in-app learning actively. Our study is the first to directly compare these embedding concepts for MLL, showing that integrating vocabulary learning into everyday smartphone interactions via UnlockApp and NotificationApp increases the number of answers. However, users show individual subjective preferences. Based on our results, we discuss the trade-off between higher content exposure and disturbance, and the related challenges and opportunities of embedding learning seamlessly into everyday mobile interactions.

The technological advances of smartphones facilitate the transformation of learning from the classroom to an activity that can happen anywhere and anytime. While micro-learning fosters ubiquitous learning, this flexibility comes at the cost of having an uncontrolled learning environment. To this point, we know little about the usage of mobile learning applications, particularly the occurrence of interruptions and the harm they cause. By diverting users' attention away from the learning task, interruptions can potentially compromise learning performance. We present a four-week in-the-wild study (N = 12) where we investigate learning behavior and the occurrence of interruptions based on device logging and experience sampling questionnaires. We recorded 276 interruptions in 327 learning sessions and found that interruption type as well as users' context influence learning sessions and the severity of the interruption (i.e., session termination likeliness). We discuss challenges and opportunities for the design of automated mechanisms to detect and mitigate interruptions in mobile learning.

Common sources of career information like websites often provide a static overall picture of a job, yet lack personal insights into the daily working life. To address this problem, we present a novel mobile career guidance method: It enables users to remotely gain an impression of different work routines by receiving several short, scheduled chat messages from a persona throughout the day. These messages were previously collected from real professionals reporting on their tasks over a week. We implemented a smartphone application to compare our message-based approach to a traditional blog entry in a two-week within-subject field study (N = 17). Users highlighted that the scheduled messages (1) enhanced their understanding of work routines by integrating career information into their own daily context and (2) offered authentic insights into the jobs. We discuss design implications for mobile career guidance systems and future opportunities for presenting chunks of information in a temporal context.

People visit public places with different intentions and motivations. While some explore it carefully, others may just want to pass or are otherwise engaged. We investigate how to exploit the inattentional blindness (IB) of indirect users in the design of public interfaces to apply to such diverse needs. Beginning with a structured literature study in the ACM Digital Library on IB, we analyzed 135 publications to derive design strategies that benefit from IB or avoid IB. Using these findings, we selected three existing interfaces for information presentation on a large public square and created two additional interfaces ourselves. We then compared users' perceptions through a self-reported photography study (N = 40). Participants followed one of four scripted profiles to imitate different user intentions, two for direct and two for indirect users. We hypothesized that direct users would recognize the interfaces, while indirect users would experience IB and ignore them. Our results show that direct users reported up to 68% of our interfaces, whereas indirect users noticed only 16%. Thus, IB can be exploited to hide interfaces from indirect users while keeping them noticeable to direct users.

Continuous location sharing (CLS) can foster intimacy, for example, for couples in long-distance relationships. However, turning off CLS can then raise suspicions. To address this, we developed nuanced sharing settings in a focus group (N = 6) and implemented them to moderate CLS in an Android app. Crucially, the app also discloses each person's current sharing settings to the partner. In a 16-day field study, four couples interacted with our app and the disclosed configurations, confirming the disclosure's positive effect on transparency. However, features obfuscating the location were considered superfluous, as participants preferred sharing their location exactly or not at all. While participants overall appreciated having the configuration options, changes in their partners' configurations raised questions about their motivations. Instead, participants would adjust the configuration for different intimacy levels (colleague vs. partner) rather than different activities when using CLS with the same person.

This reflection paper takes the 25th IUI conference milestone as an opportunity to analyse in detail the understanding of intelligence in the community: Despite the focus on intelligent UIs, it has remained elusive what exactly renders an interactive system or user interface "intelligent", also in the fields of HCI and AI at large. We follow a bottom-up approach to analyse the emergent meaning of intelligence in the IUI community: In particular, we apply text analysis to extract all occurrences of "intelligent" in all IUI proceedings. We manually review these with regard to three main questions: 1) What is deemed intelligent? 2) How (else) is it characterised? and 3) What capabilities are attributed to an intelligent entity? We discuss the community's emerging implicit perspective on characteristics of intelligence in intelligent user interfaces and conclude with ideas for stating one's own understanding of intelligence more explicitly.

Despite the extensive analysis of the consequences of interruptions caused by smartphone notifications, research on the effects on users has so far been sparse. Therefore, in this work we (1) explore concepts on preventing interruptions elicited by notification delay in a focus group; (2) implement a smartphone application manipulating the notification delay in three distinct ways varying in the degree of user-control; (3) evaluate all three concepts with 13 users in a four-week field trial. We thereby gather qualitative feedback in 52 semi-structured interviews, one per participant after each mode and an additional control week. The results show that through the intensive preoccupation with their notification management, users reflect critically about advantages and disadvantages of their continuous reachability. Based on the results from the focus group and field trial, we derive four design implications summarizing the users' experiences and suggestions on notification delay mechanisms.

The pervasive availability of media in foreign languages is a rich resource for language learning. However, learners are forced to interrupt media consumption whenever comprehension problems occur. We present BrainCoDe, a method to implicitly detect vocabulary gaps through the evaluation of event-related potentials (ERPs). In a user study (N=16), we evaluate BrainCoDe by investigating differences in ERP amplitudes during listening and reading of known words compared to unknown words. We found significant deviations in N400 amplitudes during reading and in N100 amplitudes during listening when encountering unknown words. To evaluate the feasibility of ERPs for real-time applications, we trained a classifier that detects vocabulary gaps with an accuracy of 87.13% for reading and 82.64% for listening, identifying eight out of ten words correctly as known or unknown. We show the potential of BrainCoDe to support media learning through instant translations or by generating personalized learning content.