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.

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.

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.

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 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.

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.

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.

Due to the rising popularity of streaming services, television networks are experiencing pressure to keep the attention of the younger audience. Especially in the field of Edutainment, platforms like YouTube or TED are serious competitors and require broadcasters to come up with novel ideas to engage viewers in their program. In this work, we present the augmented reality (AR) SpaceStation application, designed to supplement the viewing of educational videos about the ISS. We evaluated users' experience during the interaction with the app in a within-subject user study (N = 31) and assessed their workload. During the interaction with the SpaceStation App, participants experienced a higher workload compared to a video-only condition; nonetheless, they considered AR a valuable and enjoyable addition. This paper concludes with a discussion from the perspectives of viewers, content creators, and hosts, and states initial ideas on how to design television programs with AR content, without creating information overload.

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.

Ubiquitous technologies change the way we learn new languages. They provide easy access to multilingual media content within everyday scenarios to enhance language skills or improve vocabulary. The detection of learners’ vocabulary deficiencies is an integral part of effective language learning. However, retrieving translations during everyday media consumption causes inattention and hinders fluent learning. In this paper, we investigate Electroencephalography (EEG) to assess single-word incomprehension within non-native learning contents. In a user study (N = 10), we employed Rapid Serial Visual Presentation (RSVP) to display text while recording Event-Related Potentials (ERPs) for different word difficulties. Our results show that incomprehension of words can be detected in participants’ neural responses, as is confirmed by post hoc ratings. We conclude with use case scenarios in which our approach can facilitate seamless vocabulary deficiency detection in everyday life.

Distractions and interruptions often disrupt mobile learners. Luckily, task resumption (memory) cues can support users in resuming a learning task. These cues can have multiple forms and designs, but their effectiveness depends heavily on their adaptation to the specific learning use case. This work explores the causes of interruptions during mobile learning and outlines designs for task resumption support. We report findings from two focus groups with HCI experts (N = 4) and users of mobile learning applications (N = 3). Finally, we discuss these findings by drawing on literature, and we derive a research agenda of currently unexplored concepts. We state limitations and open questions in the domain of task resumption support for mobile learning.