Research on Cognitive Personal Informatics (CPI) is steadily growing as new wearable cognitive tracking technologies emerge on the consumer market, claiming to measure stress, focus, and other cognitive factors. At the same time, with generative AI offering new ways to analyse, visualize, and interpret cognitive data, we hypothesize that cognitive tracking will soon become as simple as measuring your heart rate during a run. Yet, cognitive data remains inherently more complex, context-dependent, and less well understood than physical activity data. This workshop brings together HCI experts to discuss critical questions, including: How can complex cognitive data be translated into meaningful metrics? How can AI support users' data sensemaking without over-simplifying cognitive insights? How can we design inclusive CPI technologies that consider inter-personal variance and neurodiversity? We will map challenges and opportunities for CPI, considering recent AI advancements, and outline a research road map for the foreseeable future.

Novel consumer neurotechnologies allow users to track their cognitive states and processes, such as attention and mental workload (MWL). However, data on these inherently complex, abstract, and invisible cognitive processes can be challenging to interpret, and little is known about how users make sense of their data. In this work, we explore how people understand and reflect on MWL through six semi-structured interviews and a follow-up experience sampling study. We examine how people conceptualize MWL, distinguish it from related concepts such as stress, what they consider high and low workload in their daily lives, and how they connect workload to emotional states. We discuss these user perceptions and identify barriers to MWL self-tracking, such as lack of trust in the data and ambiguity of the MWL concept, and propose five design guidelines to make cognitive tracking tools more intelligible and meaningful for users.

Despite its well-documented benefits, reading literature in a second language remains challenging. Graphic novels, by integrating text and images, effectively support English as a Second language (ESL) readers; however, adapting literary works into this format is resource-intensive and lacks scalability.
To address this, we developed a LangChain-based pipeline that automatically transforms a story into a graphic novel. Through a user study with 76 participants, we investigated (1) how this adaptation influences ESL readers' comprehension and narrative engagement, and (2) readers' perception of AI's role in the creative process. Results showed no significant differences in comprehension or engagement between the AI-generated graphic novel and traditional text. Although 70\% of participants recognized AI involvement, attitudes toward its role as illustrator were generally positive, despite a few cross-domain concerns. This work contributes to the understanding of AI-powered storytelling from a human-centered perspective, identifying key insights for effectively supporting readers through AI-generated visual narratives.

Where you read matters—so what if you could read literally inside your book? Reading in Virtual Reality (VR) has been shown to support deep immersion and narrative engagement. We argue that Generative Artificial Intelligence (GenAI) can significantly expand what is currently possible in VR reading by dynamically producing story-driven environments from text. In this vision paper, we present a pipeline that combines recent advances in language, audio, and 3D scene generation to allow automatically augmenting fiction reading with environmental backdrops. As the reader progresses, these AI-generated environments transition in real time, acting as cognitive props for visual imagery. We also describe a user evaluation, discuss limitations, and address implications and open questions raised by the proposed approach.

We demonstrate the manipulative potential of haptics (‘Dark Haptics’) for the future of advertising in eXtended Reality (XR). Our demonstration showcases two haptic and one pseudo-haptic interactions as a XR-based advertising strategy that can subtly manipulate user actions in this dark future of advertising work. Developed in Unreal Engine 5.5 for the Meta Quest 3, this single-user Virtual Reality experience presents an initial exploration of the manipulative qualities of haptics design that can be exploited within immersive environments. By highlighting these dark design patterns, our demonstration aims to raise awareness and initiate discussion around the ethical implications of haptic feedback for the future of work in immersive environments, particularly within advertising. Our work lays the groundwork for future research and development of tools to mitigate the potential harms and risks of dark haptics as immersive technologies become more integrated into professional and commercial environments.

Active and healthy ageing depends on maintaining physical and cognitive activity, but it is still challenging to motivate older adults to participate in regular training. This paper describes the iterative design and evaluation of a digital platform for increasing older adults' motivation to perform physical and cognitive exercises. The digital solution was designed and evaluated in four iterations with a total of 13 older adults. The first stage focused on identifying effective communication methods, including different formats of instructional delivery and feedback, as well as tone. The second stage explored the combination of physical activity with cognitively stimulating activities, such as brain games, sport, and hobbies, to find the most motivating combinations. The final stage developed the prototype further by integrating motivational elements into one coherent design, emphasizing clarity, guidance, and user agency. The final evaluation reviewed the overall design, including the importance of adaptive systems that dynamically adjust the difficulty level to align with users' physical and cognitive abilities to increase motivation. This study contributes to the growing field of participatory design within digital health interventions, aligning with best practices that emphasize the need for dynamic user involvement in all stages of development.

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.

Knowledge workers often struggle to evaluate their productivity and sense of accomplishment due to the intangible nature of their work. They rely on internal cues and personal metrics such as focus, effort, or goal completion. Traditional productivity tools overlook these dimensions, creating a gap between effort and perceived performance. This study examines how neurofeedback influences perceptions of self-accomplishment, focus, and task performance. We conducted a study in which N=20 participants completed reading and writing tasks while wearing a commercial EEG headband. Each participant completed one task with live neurofeedback and another with feedback shown afterward. While neurofeedback did not change how participants defined self-accomplishment, it encouraged reflection and awareness of cognitive effort. Some found the feedback validating, while others felt pressure or questioned its accuracy. Our findings suggest that neurofeedback can aid self-reflection in knowledge work when it is unobtrusive, ensures data comprehensibility, and supports users' existing self-evaluation strategies.

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.

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.

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.

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.

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.

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.

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.