Artificial intelligence is increasingly diffusing into design and creative education, reshaping how students ideate, iterate, and produce work. While these tools offer clear benefits in terms of speed and experimentation, they also challenge long-standing pedagogical assumptions regarding the importance of process, authorship, and studio-based learning. Educators are therefore faced with a dual task: mitigating the risks AI poses to reflective, process-oriented design while simultaneously exploring ways to meaningfully integrate these technologies into curricula. Through working group activities, this workshop examines these tensions to identify emerging strategies for the critical and transparent use of AI. Ultimately, we aim to address current knowledge gaps, establish a collaborative research agenda, and build a network to support AI collaboration in design and creative education.

Collaborate 3. Re-Inquire ?! Figure 1: D360 is a tool that enables designers to utilize 360° video in their user research process through a web based viewing and annotation tool (the D360 Viewer).Annotations made with this tool are automatically inserted into Miro, enabling designers to collaboratively analyze the 360° video material.The annotations in Miro include a back-link to the D360 viewer, which lets designers re-inquire past insights as the scope and focus of the design process change.

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases.

Computing systems are increasingly designed to adapt to users’ cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users’ behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour.

We explore the metaphorical "daily memory pill"concept - a brief pictorial lifelog recap aimed at reviving and preserving memories. Leveraging psychological strategies, we explore the potential of such summaries to boost autobiographical memory. We developed an automated lifelogging memory prosthesis and a research protocol (Automated Memory Validation "AMV") for conducting privacy-aware, in-situ evaluations. We conducted a real-world lifelogging experiment for a month (n=11). We also designed a browser "Pixel Memories"for browsing one-week worth of lifelogs. The results suggest that daily timelapse summaries, while not yielding significant memory augmentation effects, also do not lead to memory degradation. Participants' confidence in recalled content remains unaltered, but the study highlights the challenge of users' overestimation of memory accuracy. Our core contributions, the AMV protocol and "Pixel Memories"browser, advance our understanding of memory augmentations and offer a privacy-preserving method for evaluating future ubicomp systems.

Mental Workload (MWL) is a construct widely used in HCI to assess the cognitive demand users must exert to perform a task. Research in human factors, however, has suggested several issues regarding its definitions, scales, and applications. This paper, first, introduces debates surrounding the MWL concept and its most popular measure, the NASA-TLX. We present a systematic review of CHI papers involving MWL and highlight severe issues in its application. Finally, through a validation experiment, we assess the convergent validity and sensitivity of two MWL instruments—NASA-TLX and MRQ. Our findings reveal disagreements in the definitions of MWL and severe drawbacks in NASA-TLX and its applications. Our validation study also presents evidence for a lack of convergent validity and sensitivity of MWL subjective scales in HCI tasks. Our findings recommend caution when employing NASA-TLX in user studies and highlight the need for an MWL definition that is agreed upon within the HCI community.

Recent work on augmented reality (AR) has explored the use of adaptive agents to overcome attentional issues that negatively impact task performance. However, despite positive technical evaluations, adaptive agents have shown no significant improvements to user task performance in AR. Furthermore, previous works have primarily evaluated such agents using abstract tasks. In this paper, we develop an agent that observes user behaviour and performs appropriate actions to mitigate attentional issues in a realistic sense-making task in AR. We employ mixed methods to evaluate our agent in a between-subject experiment (N=60) to understand the agent’s effect on user task performance and behaviour. While we find no significant improvements in task performance, our analysis revealed that users’ preferences and trust in the agent affected their receptiveness of the agent’s recommendations. We discuss the pitfalls of autonomous agents and highlight the need to shift from designing better Human–AI interactions to better Human–AI collaborations.

Cognitive biases have been shown to play a critical role in creating echo chambers and spreading misinformation. They undermine our ability to evaluate information and can influence our behaviour without our awareness. To allow the study of occurrences and effects of biases on information consumption behaviour, we explore indicators for cognitive biases in physiological and interaction data. Therefore, we conducted two experiments investigating how people experience statements that are congruent or divergent from their own ideological stance. We collected interaction data, eye tracking data, hemodynamic responses, and electrodermal activity while participants were exposed to ideologically tainted statements. Our results indicate that people spend more time processing statements that are incongruent with their own opinion. We detected differences in blood oxygenation levels between congruent and divergent opinions, a first step towards building systems to detect and quantify cognitive biases.

Deep reading fosters text comprehension, memory, and critical thinking. The growing prevalance of digital reading on mobile interfaces raises concerns that deep reading is being replaced by skimming and sifting through information, but this is currently unmeasured. Traditionally, reading quality is assessed using comprehension tests, which require readers to explicitly answer a set of carefully composed questions. To quantify and understand reading behaviour in natural settings and at scale, however, implicit measures are needed of deep versus skim reading across desktop and mobile devices, the most prominent digital reading platforms. In this paper, we present an approach to systematically induce deep and skim reading and subsequently train classifiers to discriminate these two reading styles based on eye movement patterns and interaction data. Based on a user study with 29 participants, we created models that detect deep reading on both devices with up to 0.82 AUC. We present the characteristics of deep reading and discuss how our models can be used to measure the effect of reading UI design and monitor long-term changes in reading behaviours.

Temporal target selection requires users to wait and trigger the selection input within a bounded time window, with a selection cursor that is expected to be delayed. This task conceptualizes, for example, a variety of game scenarios such as determining the timing of shooting a projectile towards a moving object. In this work, we explore models that predict “when” users typically perform a selection (i.e., user selection distribution) and their selection error rates in such tasks. We hypothesize that users react to temporal factors including “distance”, “width”, and “delay” as how they treat the corresponding variables in spatial target selection. The derived models are evaluated in a controlled experiment and an MTurk-based online study. Our research contributes new knowledge on user behavior in temporal target selection tasks and its potential connection with its spatial correspondence. Our models and conclusions can benefit both users and designers of relevant interactive applications.

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 rapid development and ubiquity of mobile and wearable devices promises to enable researchers to monitor users’ granular emotional data in a less intrusive manner. Researchers have used a wide variety of mobile and wearable devices for this purpose, and have proposed various approaches to sense users’ emotional states. In this survey, we utilise three established digital libraries (ACM Digital Library,IEEE Xplore Digital Library, andSpringer Nature). We analysed and critically assessed the different approaches used in the three stages (perception, learning, inference) of a typical mobile emotion sensing framework, following a structured paper selection process. The contribution of this survey is three-fold; first, we document all the latest relevant literature on mobile emotion sensing research; second, we describe how mobile and wearable devices use their sensing and computing capabilities to monitor human emotions; third, we discuss challenges and opportunities of mobile emotion sensing to demonstrate the potential of this thriving field of research.

With the rapid development of mobile and wearable devices, it is increasingly possible to access users’ affective data in a more unobtrusive manner. On this basis, researchers have proposed various systems to recognize user’s emotional states. However, most of these studies rely on traditional machine learning techniques and a limited number of signals, leading to systems that either do not generalize well or would frequently lack sufficient information for emotion detection in realistic scenarios. In this paper, we propose a novel attention-based LSTM system that uses a combination of sensors from a smartphone (front camera, microphone, touch panel) and a wristband (photoplethysmography, electrodermal activity, and infrared thermopile sensor) to accurately determine user’s emotional states. We evaluated the proposed system by conducting a user study with 45 participants. Using collected behavioral (facial expression, speech, keystroke) and physiological (blood volume, electrodermal activity, skin temperature) affective responses induced by visual stimuli, our system was able to achieve an average accuracy of 89.2 percent for binary positive and negative emotion classification under leave-one-participant-out cross-validation. Furthermore, we investigated the effectiveness of different combinations of data signals to cover different scenarios of signal availability.

Implicit tendencies and cognitive biases play an important role in how information is perceived and processed, a fact that can be both utilised and exploited by computing systems. The Implicit Association Test (IAT) has been widely used to assess people’s associations of target concepts with qualitative attributes, such as the likelihood of being hired or convicted depending on race, gender, or age. The condensed version–the Brief IAT–aims to implicit biases by measuring the reaction time to concept classifications. To use this measure in HCI research, however, we need a way to construct and validate target concepts, which tend to quickly evolve and depend on geographical and cultural interpretations. In this paper, we introduce and evaluate a new method to appropriate the BIAT using crowdsourcing to measure people’s leanings on polarising topics. We present a web-based tool to test participants’ bias on custom themes, where self-assessments often fail. We validated our approach with 14 domain experts and assessed the fit of crowdsourced test construction. Our method allows researchers of different domains to create and validate bias tests that can be geographically tailored and updated over time. We discuss how our method can be applied to surface implicit user biases and run studies where cognitive biases may impede reliable results.

Voice user interfaces (VUIs) have made their way into people’s daily lives, from voice assistants to smart speakers. Although VUIs typically just react to direct user commands, increasingly, they incorporate elements of proactive behaviors. In particular, proactive smart speakers have the potential for many applications, ranging from healthcare to entertainment; however, their usability in everyday life is subject to interaction errors. To systematically investigate the nature of errors, we designed a voice-based Experience Sampling Method (ESM) application to run on proactive speakers. We captured 1,213 user interactions in a 3-week field deployment in 13 participants’ homes. Through auxiliary audio recordings and logs, we identify substantial interaction errors and strategies that users apply to overcome those errors. We further analyze the interaction timings and provide insights into the time cost of errors. We find that, even for answering simple ESMs, interaction errors occur frequently and can hamper the usability of proactive speakers and user experience. Our work also identifies multiple facets of VUIs that can be improved in terms of the timing of speech.

On-body interfaces, which leverage the human body’s surface as an input or output platform, can provide new opportunities for designing VR interaction. However, it remains unclear how on-body interfaces can best support current VR systems that mainly rely on mid-air interaction. We propose BodyOn, a collection of six design patterns that leverage combined on-body and mid-air interfaces to achieve more effective 3D interaction. Specifically, a user may use thumb-on-finger gestures, finger-on-arm gestures, or on-body displays with mid-air input, including hand movement and orientation, to complete an interaction task. To test our design concepts, we implemented example interaction techniques based on BodyOn that can assist users in various 3D interaction tasks. We further conducted an expert evaluation using the techniques as probes to elicit immediate design issues that emerge from the novel combination of on-body and midair interaction. We provide insights that can inspire and inform the design of future 3D user interfaces.

Recognising and monitoring emotional states play a crucial role in mental health and well-being management. Importantly, with the widespread adoption of smart mobile and wearable devices, it has become easier to collect long-term and granular emotion-related physiological data passively, continuously, and remotely. This creates new opportunities to help individuals manage their emotions and well-being in a less intrusive manner using off-the-shelf low-cost devices. Pervasive emotion recognition based on physiological signals is, however, still challenging due to the difficulty to efficiently extract high-order correlations between physiological signals and users' emotional states. In this paper, we propose a novel end-to-end emotion recognition system based on a convolution-augmented transformer architecture. Specifically, it can recognise users' emotions on the dimensions of arousal and valence by learning both the global and local fine-grained associations and dependencies within and across multimodal physiological data (including blood volume pulse, electrodermal activity, heart rate, and skin temperature). We extensively evaluated the performance of our model using the K-EmoCon dataset, which is acquired in naturalistic conversations using off-the-shelf devices and contains spontaneous emotion data. Our results demonstrate that our approach outperforms the baselines and achieves state-of-the-art or competitive performance. We also demonstrate the effectiveness and generalizability of our system on another affective dataset which used affect inducement and commercial physiological sensors.

Smart speakers have become exceedingly popular and entered many people's homes due to their ability to engage users with natural conversations. Researchers have also looked into using smart speakers as an interface to collect self-reported health data through conversations. Responding to surveys prompted by smart speakers requires users to listen to questions and answer in voice without any visual stimuli. Compared to traditional web-based surveys, where users can see questions and answers visually, voice surveys may be more cognitively challenging. Therefore, to collect reliable survey data, it is important to understand what types of questions are suitable to be administered by smart speakers. We selected five common survey questionnaires and deployed them as voice surveys and web surveys in a within-subject study. Our 24 participants answered questions using voice and web questionnaires in one session. They then repeated the same study session after 1 week to provide a "retest'' response. Our results suggest that voice surveys have comparable reliability to web surveys. We find that, when using 5-point or 7-point scales, voice surveys take about twice as long as web surveys. Based on objective measurements, such as response agreement and test-retest reliability, and subjective evaluations of user experience, we recommend that researchers consider adopting the binary scale and 5-point numerical scales for voice surveys on smart speakers.

Electrodermal activity data is widely used in HCI to capture rich and unbiased signals. Results from related fields, however, have suggested several methodological issues that can arise when practices do not follow established standards. In this paper, we present a systematic methodological review of CHI papers involving the use of EDA data according to best practices from the field of psychophysiology, where standards are well-established and mature. We found severe issues in our sample at all stages of the research process. To ensure the validity of future research, we highlight pitfalls and offer directions for how to improve community standards.

Attentional tunneling describes a phenomenon in Augmented Reality (AR) where users excessively focus on virtual content while neglecting their physical surroundings. This leads to the concern that users could neglect hazardous situations when using AR applications. However, studies have often confounded the role of the virtual content with the role of the associated task in inducing attentional tunneling. In this paper, we disentangle the impact of the associated task and of the virtual content on the attentional tunneling effect by measuring reaction times to events in two user studies. We found that presenting virtual content did not significantly increase user reaction times to events, but adding a task to the content did. This work contributes towards our understanding of the attentional tunneling effect on handheld AR devices, and highlights the need to consider both task and context when evaluating AR application usage.

This paper investigates integration, coordination, and transition strategies of gaze and hand input for 3D object manipulation in VR. Specifically, this work aims to understand whether incorporating gaze input can benefit VR object manipulation tasks, and how it should be combined with hand input for improved usability and efficiency. We designed four gaze-supported techniques that leverage different combination strategies for object manipulation and evaluated them in two user studies. Overall, we show that gaze did not offer significant performance benefits for transforming objects in the primary working space, where all objects were located in front of the user and within the arm-reach distance, but can be useful for a larger environment with distant targets. We further offer insights regarding combination strategies of gaze and hand input, and derive implications that can help guide the design of future VR systems that incorporate gaze input for 3D object manipulation.

Automatically detecting attentional states is a prerequisite for designing interventions to manage attention - knowledge workers' most critical resource. As a first step towards this goal, it is necessary to understand how different attentional states are made discernible through visible cues in knowledge workers. In this paper, we demonstrate the important facial cues to detect attentional states by evaluating a data set of 15 participants that we tracked over a whole workday, which included their challenge and engagement levels. Our evaluation shows that gaze, pitch, and lips part action units are indicators of engaged work; while pitch, gaze movements, gaze angle, and upper-lid raiser action units are indicators of challenging work. These findings reveal a significant relationship between facial cues and both engagement and challenge levels experienced by our tracked participants. Our work contributes to the design of future studies to detect attentional states based on facial cues.

Inspired by the increasing prevalence of digital voice assistants, we demonstrate the feasibility of using voice interfaces to deploy and complete crowd tasks. We have developed Crowd Tasker, a novel system that delivers crowd tasks through a digital voice assistant. In a lab study, we validate our proof-of-concept and show that crowd task performance through a voice assistant is comparable to that of a web interface for voice-compatible and voice-based crowd tasks for native English speakers. We also report on a field study where participants used our system in their homes. We find that crowdsourcing through voice can provide greater flexibility to crowd workers by allowing them to work in brief sessions, enabling multi-tasking, and reducing the time and effort required to initiate tasks. We conclude by proposing a set of design guidelines for the creation of crowd tasks for voice and the development of future voice-based crowdsourcing systems.

In this paper, we demonstrate the existence of a bidirectional causal relationship between smartphone application use and user emotions. In a two-week long in-the-wild study with 30 participants we captured 502,851 instances of smartphone application use in tandem with corresponding emotional data from facial expressions. Our analysis shows that while in most cases application use drives user emotions, multiple application categories exist for which the causal effect is in the opposite direction. Our findings shed light on the relationship between smartphone use and emotional states. We furthermore discuss the opportunities for research and practice that arise from our findings and their potential to support emotional well-being.

Studies using augmented reality (AR) technology have suggested that users focus excessively on the virtual content in the AR environment at the expense of the physical world around them. This has implications related to the design of installations that aim to incorporate the user's physical environment as part of the AR experience. To better understand how user attention is managed in an AR environment, we present an observational study of Rewild Our Planet, a multi-modal installation that combined video, audio, a human docent and mobile AR to promote awareness about environmental issues. We found that, while AR was successful in engaging visitors, it drew attention away from other modalities within the installation. This impacts the work of the human docent and affects how visitors absorb information presented in the installation. Based on these observations, we present guidelines to inform the design of future AR-supported installations with the aim of minimizing or taking advantage of the observed attentional issues.

Cognitive performance fluctuates throughout the day partly due to our “inner clock”. Systems that are aware of these fluctuations can adapt to their users’ momentary capacity and adjust task difficulty accordingly. Here, we evaluate the use of performance measures obtained from mobile game data to estimate players’ varying alertness. We developed a smartphone game that emulates three validated tasks for cognitive performance and conducted an in-the-wild study over two weeks with 30 participants to collect performance measures and subjective ground truth on alertness levels. Our results show that players’ performance can be explained by a generative model which is based on two established alertness-modulating processes, namely the homeostatic process and circadian alertness fluctuations. Our method can be used to implicitly model players’ alertness levels as a basis for cognition-aware applications.

While text tends to lead a rather static life on paper and screens, virtual reality (VR) allows readers to interact with it in novel ways: the reading surface is no longer confined to a 2D plane. We conducted two user studies, in which we assessed text rendered on different surface shapes in VR and their effects on legibility and the reading experience. Comparing differently curved surfaces, these studies disclose the impact of warp angles and view box widths on reading comfort, speed, and distraction. Our results suggest that text should be warped around the horizontal rather than the vertical axis, and we provide recommendations for the extent of warp and view box width. In a proof-of-concept application, we used everyday 3D objects as text canvases and studied them through an information-seeking task. The studies’ implications inform VR interfaces and, more generally, the rendering of text on 3D objects.

Selection studies are prevalent and indispensable for VR research. However, due to the tedious and repetitive nature of many such experiments, participants can become disengaged during the study, which is likely to impact the results and conclusions. In this work, we investigate participant disengagement in VR selection experiments and how this issue affects the outcomes. Moreover, we evaluate the usefulness of four engagement strategies to keep participants engaged during VR selection studies and investigate how they impact user performance when compared to a baseline condition with no engagement strategy. Based on our findings, we distill several design recommendations that can be useful for future VR selection studies or user tests in other domains that employ similar repetitive features.

The presence of fully-occluded targets is common within virtual environments, ranging from a virtual object located behind a wall to a datapoint of interest hidden in a complex visualization. However, efficient input techniques for locating and selecting these targets are mostly underexplored in virtual reality (VR) systems. In this paper, we developed an initial set of seven techniques techniques for fully-occluded target selection in VR. We then evaluated their performance in a user study and derived a set of design implications for simple and more complex tasks from our results. Based on these insights, we refined the most promising techniques and conducted a second, more comprehensive user study. Our results show how factors, such as occlusion layers, target depths, object densities, and the estimation of target locations, can affect technique performance. Our findings from both studies and distilled recommendations can inform the design of future VR systems that offer selections for fully-occluded targets.

Mobile self-reports are a popular technique to collect participant labelled data in the wild. While literature has focused on increasing participant compliance to self-report questionnaires, relatively little work has assessed response accuracy. In this paper, we investigate how participant context can affect response accuracy and help identify strategies to improve the accuracy of mobile self-report data. In a 3-week study we collect over 2,500 questionnaires containing both verifiable and non-verifiable questions. We find that response accuracy is higher for questionnaires that arrive when the phone is not in ongoing or very recent use. Furthermore, our results show that long completion times are an indicator of a lower accuracy. Using contextual mechanisms readily available on smartphones, we are able to explain up to 13% of the variance in participant accuracy. We offer actionable recommendations to assist researchers in their future deployments of mobile self-report studies.

As the day progresses, cognitive functions are subject to fluctuations. While the circadian process results in diurnal peaks and drops, the homeostatic process manifests itself in a steady decline of alertness across the day. Awareness of these changes allows the design of proactive recommender and warning systems, which encourage demanding tasks during periods of high alertness and flag accident-prone activities in low alertness states. In contrast to conventional alertness assessments, which are often limited to lab conditions, bulky hardware, or interruptive self-assessments, we base our approach on eye blink frequency data known to directly relate to fatigue levels. Using electrooculography sensors integrated into regular glasses' frames, we recorded the eye movements of 16 participants over the course of two weeks in-the-wild and built a robust model of diurnal alertness changes. Our proposed method allows for unobtrusive and continuous monitoring of alertness levels throughout the day.

Finding relevant documents is essential for researchers of all disciplines. We investigated an approach for supporting searchers in their relevance decision in a digital library by automatically highlighting the most important keywords in abstracts. We conducted an eye-tracking study with 25 subjects and observed very different search and reading behavior which lead to diverse results. Some of the participants liked that highlighted abstracts accelerate their relevance decision, while others found that they disturb the reading flow. What many agree on is that the quality of highlighting is crucial for trust and system credibility.

History of conversations through instant messaging (IM) contains abundant information about the communication patterns of the dyad, including conversation partners' mutual responsiveness to messages. We have, however, not seen many examinations of using such information in modeling mobile users' responsiveness in IM communication. In this paper, we present an in-the-wild study, in which we leverage participants' IM messaging logs to build models predicting their general responsiveness. Our models based on data from 33 IM user achieved an accuracy of up to 71% (AUROC). In particular, we show that 90-day IM-communication patterns, in general, outperformed their 14-day equivalent in our prediction models, indicating better coherence between long-term IM patterns with their general communication experience.

In the era of ubiquitous computing, people expect applications to work across different devices. To provide a seamless user experience it is therefore crucial that interfaces and interactions are consistent across different device types. In this paper, we present a method to create gesture sets that are consistent and easily transferable. Our proposed method entails 1) the gesture elicitation on each device type, 2) the consolidation of a unified gesture set, and 3) a final validation by calculating a transferability score. We tested our approach by eliciting a set of user-defined gestures for reading with Rapid Serial Visual Presentation (RSVP) of text for three device types: phone, watch, and glasses. We present the resulting, unified gesture set for RSVP reading and show the feasibility of our method to elicit gesture sets that are consistent across device types with different form factors.

Smart glasses are increasingly being used in professional contexts. Having key applications such as short messaging and newsreader, they enable continuous access to textual information. In particular, smart glasses allow reading while performing other activities as they do not occlude the user's world view. For efficient reading, it is necessary to understand how a text should be presented on them. We, therefore, conducted a study with 24 participants using a Microsoft HoloLens to investigate how to display text on smart glasses while walking and sitting. We compared text presentation in the top-right, center, and bottom-center positions with Rapid Serial Visual Presentation (RSVP) and line-by-line scrolling. We found that text displayed in the top-right of smart glasses increases subjective workload and reduces comprehension. RSVP yields higher comprehension while sitting. Conversely, reading with scrolling yields higher comprehension while walking. Insights from our study inform the design of reading interfaces for smart glasses.

To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people's reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user's day.

Reflecting on previous activities can improve episodic memory and well-being. While manually recording activities and experiences is not always feasible, there is a range of mobile sensors that allow implicit recording of users' lives. In this work, we investigate how reflection on daily summaries created from implicitly collected data improves episodic memory. Therefore, we built ReflectiveDiary, an Android application that collects personal data streams to create daily summaries automatically. Over the course of 16 days, we collected data from 11 participants using information, such as calls, messages, and calendar data to help people recollect their activities. By comparing reflected with non-reflected days, we show that reflecting on implicitly collected data improves remembering of events and their surrounding details. We further present an analysis of different types of memory cues and their usefulness to inform the design of reflective tools that help people to improve their episodic memory.

Learning a foreign language is a daunting and time-consuming task. People often lack the time or motivation to sit down and engage with learning content on a regular basis. We present an investigation of microlearning sessions on mobile phones, in which we focus on session triggers, presentation methods, and user context. Therefore, we built an Android app that prompts users to review foreign language vocabulary directly through notifications or through app usage across the day. We present results from a controlled and an in-the-wild study, in which we explore engagement and user context. In-app sessions lasted longer, but notifications added a significant number of "quick" learning sessions. 37.6% of sessions were completed in transit, hence learning-on-the-go was well received. Neither the use of boredom as trigger nor the presentation (flashcard and multiple-choice) had a significant effect. We conclude with implications for the design of mobile microlearning applications with context-awareness.

People use more and more applications and devices that quantify daily behavior such as the step count or phone usage. Purely presenting the collected data does not necessarily support users in understanding their behavior. In recent research, concepts such as learning by reflection are proposed to foster behavior change based on personal data. In this paper, we introduce user-made predictions to help users understand personal behavior patterns. Therefore, we developed an Android application that tracks users' screen-on and unlock patterns on their phone. The application asks users to predict their daily behavior based on their former usage data. In a user study with 12 participants, we showed the feasibility of leveraging user-made predictions in a quantified self approach. By trying to improve their predictions over the course of the study, participants automatically discovered new insights into personal behavior patterns.

Reviewing lifelogging data has been proposed as a useful tool to support human memory. However, the sheer volume of data (particularly images) that can be captured by modern lifelogging systems makes the selection and presentation of material for review a challenging task. We present the results of a five-week user study involving 16 participants and over 69,000 images that explores both individual requirements for video summaries and the differences in cognitive load, user experience, memory experience, and recall experience between review using video summarisations and non-summary review techniques. Our results can be used to inform the design of future lifelogging data summarisation systems for memory augmentation.

Boredom is a common human emotion which may lead to an active search for stimulation. People often turn to their mobile phones to seek that stimulation. In this paper, we tackle the challenge of automatically inferring boredom from mobile phone usage. In a two-week in-the-wild study, we collected over 40,000,000 usage logs and 4398 boredom self-reports of 54 mobile phone users. We show that a user-independent machine-learning model of boredom --leveraging features related to recency of communication, usage intensity, time of day, and demographics-- can infer boredom with an accuracy (AUCROC) of up to 82.9%. Results from a second field study with 16 participants suggest that people are more likely to engage with recommended content when they are bored, as inferred by our boredom-detection model. These findings enable boredom-triggered proactive recommender systems that attune their users' level of attention and need for stimulation.

Social norm has it that people are expected to respond to mobile phone messages quickly. We investigate how attentive people really are and how timely they actually check and triage new messages throughout the day. By collecting more than 55,000 messages from 42 mobile phone users over the course of two weeks, we were able to predict people's attentiveness through their mobile phone usage with close to 80% accuracy. We found that people were attentive to messages 12.1 hours a day, i.e. 84.8 hours per week, and provide statistical evidence how very short people's inattentiveness lasts: in 75% of the cases mobile phone users return to their attentive state within 5 minutes. In this paper, we present a comprehensive analysis of attentiveness throughout each hour of the day and show that intelligent notification delivery services, such as bounded deferral, can assume that inattentiveness will be rare and subside quickly.

With an increasing number of new camera devices entering the market, lifelogging has turned into a viable everyday practice. The promise of comprehensively capturing our life's happenings has caused adoption rates to grow, but approaches to do so greatly differ. In this paper we evaluate existing visual lifelogging capture approaches through a user study with two main capture dimensions: (1) comparing the body position where a lifelogging camera is worn: head versus chest (2) comparing the media captures: video versus stills. We equipped 30 participants with cameras on their heads and chests. That data was evaluated by subjective user ratings as well as by objective image processing analysis. Our findings indicate that (1) chest-worn devices are more stable and contain less motion blur through which feature detection by image processing algorithms works better than from head-worn cameras; 2) head-worn video cameras, however, seem to be the better choice for lifelogging as they capture more important autobiographical cues than chest-worn devices, e.g., faces that have been shown to be most relevant for recall.

Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them.

Many embedded platforms that support the creation of interactive smart objects have become available over the last years. Arduino, Raspberry Pi, electric imp, mbed, MSP430, and .NET Gadgeteer are examples of hardware platforms with very different properties and capabilities. In order to make interactive artifacts additional sensors, actuators, and networking elements are available for the different platforms. Additionally there are different software environments and development tools that support developers in creating custom applications for embedded systems. It is apparent that one size doesn't fit all and that choosing the right platform and tools is an important step towards an effective solution. In this TEI2014 studio we first provide an overview of available platforms and tools that allow developers to create novel and tangible interactive systems. We will present and discuss developing environments, with a specific focus on browser based programming tools and social coding. In a second step we will have 2 hands-on sessions, where in each we use a different platform and create an initial functional prototype. The aim is to provide the participants with an overview of existing embedded development tools suitable for creating interactive artifacts and to provide some hands on-experience with different new platforms.

It has become extremely easy for developers to build custom software for smartphone and tablet computers. However, it is still hard to extend those devices with external electronics, e.g. additional sensors and actuators. In the moment when external hardware can be easily attached to phones and tablets a wide new application space will be opened up. With WebClip we present a device offering digital and analogue I/O ports that can be controlled and monitored by just clipping the device onto a capacitive touch screen. A web page in the browser of the touch screen device is used to control the bi-directional communication. Data from the WebClip to the device is sent by emulating touches on the screen whereas the reverse direction uses light sensors on the bottom side of the clip to receive light sequences emitted by the web page. A simple Javascript API is offered to build custom web applications. We have successfully tested our prototype with a variety of phones and tablet computers and report on performance and limitations.

In this paper, we investigate how smartphone applications, in particular web browsers, are used on mobile phones. Using a publicly available widget for smart phones, we recorded app usage and the phones' acceleration and orientation from 1,330 devices. Combining app usage and sensor data we derive the device's typical posture while different apps are used. Analyzing motion data shows that devices are moved more while messaging and navigation apps are used as opposed to browser and other common applications. The time distribution between landscape and portrait depicts that most of the landscape mode time is used for burst interaction (e.g., text entry), except for Media apps, which are mostly used in landscape mode. Additionally, we found that over 31% of our users use more than one web browser. Our analysis reveals that the duration of mobile browser sessions is longer by a factor of 1.5 when browsers are explicitly started through the system's launcher in comparison to being launched from within another app. Further, users switch back and forth between apps and web browsers, which suggest that a tight and smooth integration of web browsers with native apps can improve the overall usability. From our findings we derive design guidelines for app developers.

User participation has transformed the way news travel the globe. With the rise of the 'Web 2.0' phenomenon users have been empowered with the means of creating and distributing informational items, which we call social feeds. Platforms like Twitter and Facebook provide a variety of tools to facilitate real-time communication among people. But social sites are not limited to personal chat; they also provide an effective means for organizing large groups of people in response to catastrophic disasters. Monitoring these feeds can provide time-critical information, but can easily lead to information overload due to the large amount of data being shared.