In digital knowledge work, flow promises not just productivity; it offers a pathway to well-being. Yet despite decades of flow research in HCI, we know little about how to design digital interventions that support it. In this work, we foreground lived interventions - everyday practices workers already use to foster flow - to uncover overlooked opportunities and chart new directions for digital intervention design. Specifically, we report findings from two studies: (1) a reflexive thematic analysis of open-ended survey responses (n = 160), surfacing 38 lived interventions across four categories: environment, organization, task shaping, and personal readiness; and (2) a quantitative online survey (n = 121) that validates this repertoire, identifies which interventions are broadly endorsed versus polarizing, and elicits visions of technological support. We contribute empirical insights into how digital workers cultivate flow, situate these lived interventions within existing literature, and derive design opportunities for future digital flow interventions.

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

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.

The paradigm of web search is currently shifting from reactive information retrieval to Agentic AI, where proactive systems autonomously synthesise information to assist users. However, for these agents to be effective, they must understand which user parameters drive behaviour to resolve the personalisation cold-start problem. While cognitive architecture is a recognised factor, empirical evidence linking specific traits to web search interaction remains unclear. This paper investigates the Verbal-Imagery (V-I) cognitive style dimension and its influence on proactive search behaviour, mental workload (MWL), and overall search user interface (SUI) alignment. Through a controlled user study $(N=20)$, web search behaviours were evaluated using interaction logs and think-aloud protocols, while MWL and usability were assessed via NASA-TLX and the System Usability Scale (SUS). Our findings reveal that verbalisers and imagers adopt statistically distinct navigational preferences: verbalisers prefer sporadic, reactive interactions, while imagers rely on structured, proactive synthesis such as the Knowledge Panel. It is worth noting that qualitative data identifies a “trust gap” in AI-generated overviews based on cognitive modality preferences. These results demonstrate that the V-I dimension is a critical parameter for user modelling in agentic systems. We conclude by proposing requirements for user-aware agentic information retrieval, providing a framework for agents to dynamically adapt their representation strategies to minimise cognitive friction and enhance trust in proactive computing environments.

Recent work has moved from measuring Mental Workload (MWL) within the context of successfully achieving a solitary task, to understanding the role it plays across our daily lives, where recent work proposed that people move through MWL Cycles. We sought to investigate the proposed MWL cycle, and explore how it competes with other factors for time management. We interviewed 42 participants about how they managed their tasks across the day in relation to MWL and other factors. Prior to these interviews, participants engaged in a preparation week, recording and rating their different tasks to ground those interviews in real examples. Based on an interpretative phenomenological analysis, this paper contributes an explication of five superordinate themes for how they MWL as a lens for time management: Priority, Agency, Mood, Fatigue, and Productivity. We also contribute an updated model overlaying how these factors relate to the MWL Cycle.

This meet-up will bring together researchers and practitioners interested in the timely intersection of neuroscience and human–computer interaction (NeuroHCI). Advances in performance and accessibility of methods such as EEG, fNIRS, BCIs, and biosensing open new possibilities for design and interaction while also raising conceptual, technical, and ethical challenges. The session will employ engaging, interactive activities to maximize dialog, including an exercise that invites participants to experience embodied approaches to interaction. Our goal is to catalyze interdisciplinary collaboration, strengthen and grow the NeuroHCI community, and identify promising directions for future research and practice.

Researchers often attribute social media’s appeal to its ability to elicit flow experiences of deep absorption and effortless engagement. Yet prolonged use has also been linked to distraction, fatigue, and lower mood. This paradox remains poorly understood, in part because prior studies rely on habitual or one-shot reports that ask participants to directly attribute flow to social media. To address this gap, we conducted a five-day field study with 40 participants, combining objective smartphone app tracking with daily reconstructions of flow-inducing activities. Across 673 reported flow occurrences, participants rarely associated flow with social media (2%). Instead, heavier social media use predicted fewer daily flow occurrences. We further examine this relationship through the effects of social media use on fatigue, mood, and motivation. Altogether, our findings suggest that flow and social media may not align as closely as assumed - and might even compete - underscoring the need for further research.

Playing games has been shown to be an effective method of post-work recovery. Previous research has shown that gameplay with high cognitive involvement is effective for recovery. This finding conflicts with models of mental workload (MWL), which suggest that people feel best when cycling between high and low MWL. To unpack the relationship between recovery and mental workload, we designed a lab experiment where 40 participants experienced different combinations of high and low MWL while undertaking both work tasks and recovery gameplay, and we collected both self-report and physiological (fNIRS) data. Results showed that high and low MWL games created different impacts on recovery, depending on the MWL of the prior work task. While fNIRS measurements of MWL varied as expected during work tasks, experience of MWL when playing games was not evident in the prefrontal cortex. We conclude by discussing the relationship between mental workload and theories of recovery.

Flow, a state of deep task engagement, is associated with optimal experience and well-being, making its detection a prolific HCI research focus. While physiological sensors show promise for flow detection, most studies are lab-based. Furthermore, brain sensing during natural work remains unexplored due to the intrusive nature of traditional EEG setups. This study addresses this gap by using wearable, around-the-ear EEG sensors to observe flow during natural knowledge work, measuring EEG throughout an entire day. In a semi-controlled field experiment, participants engaged in academic writing or programming, with their natural flow experiences compared to those from a classic lab paradigm. Our results show that natural work tasks elicit more intense flow than artificial tasks, albeit with smaller experience contrasts. EEG results show a well-known quadratic relationship between theta power and flow across tasks, and a novel quadratic relationship between beta asymmetry and flow during complex, real-world tasks.

A key challenge for new reviewers is getting the tone and structure of a review right. A skilful reviewer will provide enough information in their review to help editors or Associate Chairs decide about including a paper in a journal or proceedings. This course will help participants understand a) the expectations of different submission types, b) how different venues make decisions, and c) identifying strong contributions, robust methodologies, and clear writing to create reviews for these different settings. Participants will critique anonymised but real reviews, and try to guess the venue they are written for and the recommendation they make.

Generative AI enables users to explore limitless possibilities, but its open-ended nature introduces ambiguity that differs from traditional GUIs. As general users integrate GenAI into their personal and professional workflows, challenges around prompting and usability have emerged. This study examines these challenges through the lens of metacognition, specifically the metacognitive abilities of monitoring and control - self-awareness and task decomposition, during intent-based interactions with a GenAI tool, exploring how these abilities influence control over three types of tasks. Our findings reveal that self-awareness is more critical in simpler tasks, while task decomposition becomes crucial as task complexity and output novelty increase. Additionally, we investigate underlying trust in AI, finding contradictions between user's metacognitive awareness and their faith across tasks, revealing the role of output evaluation with domain knowledge. Based on these insights, we offer recommendations for enhancing metacognitive support in GenAI tools and suggest directions for future research.

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.

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.

Cognitive style has been shown to influence users’ interaction with search interfaces. However, as a fundamental dimension of cognitive styles, the relationship between the Verbal-Imagery (VI) cognitive style dimension and search behaviour has not been studied thoroughly, and it is not clear whether VI cognitive style can be used to inform search user interface design. We present a study (N=29), investigating how search behaviour and mental workload (MWL) changes relate to VI cognitive styles by examining participants’ search behaviour across three increasingly complex tasks. MWL was subjectively rated by participants, and blood oxygenation changes in the prefrontal cortex were measured using functional near-infrared spectroscopy (fNIRS). Our results revealed a significant difference between verbalisers and imagers in search behaviour. In particular, verbalisers preferred a Sporadic navigation style and adopted the Scanning strategy as they processed information, according to their viewing and bookmarking patterns, whereas imagers preferred the Structured navigation style and reading information in detail. The fNIRS data showed that verbalisers had significantly higher blood oxygenation in the prefrontal cortex when using the same search interface, suggesting a higher MWL than imagers. When based on task complexity bias, the search time significantly increased as task complexity increased, but there were no significant differences in search behaviours. Our study indicated that VI cognitive styles have a noticeable and stronger impact on users’ searching behaviour and their MWL when interacting with the same interface than task complexity, which can be considered further in future search behaviour studies and search user interface design.

With the growing concern for the health of ageing populations, much research continues to look at the impact of cognitive training, particularly in relation to cognitive decline. We sought to use novel techniques, including augmented reality and portable neurotechnology, to evaluate the impact of a dynamically adjusting cognitive training programme, in comparison to a statically challenging alternative. Before and after an 8-week training period, and at a 5-week follow-up, we used portable functional Near Infrared Spectroscopy to examine mental workload in a mixed battery of cognitive and transfer tasks. A recently developed tablet-based task was used to identify changes in cognitive misbinding. Augmented Reality was used to create a supermarket shopping experience, as a more ecologically valid and realistic transfer task relating to an everyday task relating to independence that quickly becomes difficult with cognitive decline. The analyses showed a decreased mental workload within the dorsolateral prefrontal cortex and that participants considerably increased their performance in the trained task. Some results were maintained at the 5-week follow-up assessment. In terms of transfer, we observed reliable group differences immediately after training completion, which were mainly driven by distinct conditions. Some behavioural memory gains were maintained during the follow-up. The use of novel technologies brought new insights into the effects produced by the dynamic computerised cognitive training programme, which has potential future applications in cognitive decline screening and prevention.

Forecasting chaotic systems is a notably complex task, which in recent years has been approached with reasonable success using reservoir computing (RC), a recurrent network with fixed random weights (the reservoir) used to extract the spatio-temporal information of the system. This work presents a hybrid quantum reservoir-computing (HQRC) framework, which replaces the reservoir in RC with a quantum circuit. The modular structure and measurement feedback in the circuit are used to encode the complex system dynamics in the reservoir states, from which classical learning is performed to predict future dynamics. The noiseless simulations of HQRC demonstrate valid prediction times comparable to state-of-the-art classical RC models for both the Lorenz63 and double-scroll chaotic paradigmatic systems and adhere to the attractor dynamics long after the forecasts have deviated from the ground truth.

Digital manufacturing technologies (DMTs) have the potential to transform industry productivity, but their introduction into the workplace is often a complex process, requiring not only technical expertise but also an awareness of ethical and societal challenges surrounding human–system integration. Concerns about the introduction of new technology have been prevalent throughout history, and exploring public perceptions of these technologies can provide insight to help address such cultural anxieties. However, evaluating user perceptions of futuristic technology is difficult, requiring novel approaches to provide context and understanding. To explore users' perceptions of future DMTs, we applied the ContraVision technique in a questionnaire‐based study. Participants viewed films, representing fictionalized utopic and dystopic visions of what the future of these DMTs might involve, and a questionnaire probed the perceptions of the technologies afterward. Findings showed that irrespective of the way technology was portrayed, participants had concerns about the ethical and responsible implementation of these tools. Participant responses were analyzed to identify key challenges for policy surrounding DMT implementation in the future of manufacturing.