While global stress levels have been increasing, there has also been a notable rise in the use of stress management applications. These applications commonly monitor the user's stress levels and visualize this information or offer digital versions of evidence-based methods for stress relief. Additionally, recent advances in artificial intelligence provide significant potential to enhance stress management applications through increased personalization and the facilitation of social interaction. In many regards, these applications are “user-friendly,” but there seems to have been little effort to include users in the first stages of development. We believe that by including potential users early in the development process, promising designs can be uncovered that lead to a better appropriation of the application into users’ daily lives. In this paper, we address the research gap by exploring how potential users anticipate using an intelligent stress management application. We achieve this by allowing seven participants (n = 7) to imagine having access to such an app for two weeks, and then conducting interviews with them about their experience. Our main findings are that a) the role of an intelligent stress management application is multi-faceted, b) the social interaction should be friendly and feel genuine, c) there is a general need for task management and scheduling that takes stress levels and energy into consideration and d) designing for smartphone is challenging because it is often a source of stress, but an intelligent system can decrease such stress.
Promoting sustainability through creative approaches offers a pathway to achieving sustainability goals, yet the resulting solutions often lack efficiency and effectiveness. Addressing this challenge, we introduce SAIESE, a Creativity Support Tool (CST) leveraging a Large Language Model (LLM) to support the creative process, the generation, evaluation, and selection of sustainable ideas. Our work articulates LLMs role in exploration and selection, focusing on sustainable ideas based on three metrics - Environmental Impact, Economic Benefits, and Social Effects. Our evaluation demonstrates that the prototype outperforms human evaluative tasks in usability, creative thinking, and decision-making while reducing cognitive workload. However, its effectiveness in promoting creativity depends on the clarity of ideas, underlining the importance of human collaboration in ambiguous contexts. This work articulates the potential of AI in creativity, expanding current research on CSTs by highlighting sustainability-oriented creative processes and decision-making support.
This paper explores the potential of artificial intelligence (AI) to enhance employee experience in future industrial work environments, with a focus on promoting employee well-being and work meaningfulness. Drawing upon well-known theories and schools of thought such as Motivation-Hygiene Theory, Human-Factors and Ergonomics, Self-Determination Theory and Aristotelian Philosophy, the paper presents the different roles for AI to improve employee satisfaction, promote engagement, and flourishing. Firstly, AI can be introduced to remove pain points in work tasks inducing higher employee satisfaction. Secondly, AI can be adopted to enhance human agency to improve work engagement. Finally, AI can be developed to cultivate employees’ sense of purpose, self-worth, and flourishing at work. This interdisciplinary perspective shows how AI design could build on ergonomics and expand its scope to engaging experiences and even to flourishing, the ultimate level of human well-being.
This study investigates how a selected regional public healthcare system (RPHS) in Sweden prepared for and responded during the pre-crisis phase preceding the COVID-19 pandemic. Employing a qualitative methodology, eight semi-structured interviews were conducted with employees from the RPHS, and the data were analyzed using thematic analysis. Six key themes emerged from the analysis: understanding the situation, preparations, applying the pandemic preparedness plan, organizational changes, challenges, and co-operation. A significant contribution of this study is the identification of three distinct sub-phases within the pre-crisis phase: (1) actions undertaken preparing for an unknown future pandemic, (2) precautionary measures initiated following the emergence of initial reports of COVID-19, and (3) escalated preparations when a local outbreak became inevitable. The findings were further contextualized through the theoretical lenses of High Reliability Organizations (HRO) and Resilience Engineering (RE), offering deeper insights into the underlying processes of the pre-crisis phase. For instance, the HRO principle of reluctance to simplify was reflected in the theme understanding the situation, where the RPHS's initial underestimation of the crisis delayed critical organizational changes, such as the establishment of the regional medical command and control team for disaster response (RMC2). Similarly, the RE concept of anticipating potential challenges was evident in the themes of preparations and challenges, highlighting how the RPHS anticipated the need for specialized competencies but failed to sufficiently predict the demand for personal protective equipment. This paper contributes to the area of cognitive ergonomics by analyzing how healthcare and crisis management professionals perceived, anticipated, and responded to an unfolding crisis (i.e., the upcoming local outbreak of COVID-19). Using empirical data and theoretical lenses from HRO and RE, it is revealed how cognitive processes, such as sensemaking, attention allocation, and decision-making under uncertainty, shaped outcomes. These insights can inform the design of future systems, interfaces, and training aimed at supporting cognitive work in complex, high-stakes environments.
Inspired by the growing development of Creativity Support Tools (CSTs) to enhance creative work over the past ten years, we investigate the evolution of these tools in supporting divergent and convergent thinking based on the Double Diamond model. However, the approaches developed so far do not represent the complete design space, indicating the existence of a limitation during the creative process. This article presents the Divergent-Convergent Thinking Framework (DCTF) to support an understanding of the composition of the creative spectrum. Based on a literature review (N=96), our framework synthesizes key categories of creativity and, particularly, research on CSTs. We propose six dimensions and four challenges and opportunities to guide the analysis of already developed CSTs, the development of future CSTs, and how to integrate them into users' workflow. To test its relevance, we present a possible scenario for the framework's applicability in the context of sustainable design-oriented creativity. We demonstrate how the framework guides the selection of ideas, supporting the cyclical transition between divergent and convergent thinking.
Computer-Assisted Qualitative Data Analysis Software (CAQDAS) has profoundly transformed research practices, particularly within the Grounded Theory Method (GTM) approach. However, when used in collaborative settings, these tools create tensions—such as cognitive overload, loss of traceability, and coordination difficul- ties—stemming from a mismatch between the functional rigidity of existing software and the interpretive flexibility inherent to cooperative work. An ethnographic study with students and researchers highlights these tensions by examining the coordination mechanisms through the protocols and artifacts utilized. In response to these findings, we propose design directions for a digital artifact better suited to support shared reasoning and document collective adjustments through a participatory approach grounded in the actual practices of GTM users.
The use of advanced technologies that collaborate with humans in work contexts is increasing. Designing these complex systems to support smooth collaboration and a good user experience can be challenging. The goal of this study was two-fold: to understand which kinds of human factors and ergonomics (HFE) methods would be suitable for user-based evaluation of a proof-of-concept (PoC) and to examine the experience of interacting with a multipurpose robot. To achieve these objectives, three HFE methods were applied: a questionnaire, interviews, and observation. Additionally, a Wizard of Oz approach was utilised. As a result, qualitative HFE approaches such as interviews and observations were seen as beneficial in the evaluation of novel PoCs in an early design phase. Regarding the robot system, the results revealed that the interaction was experienced as fluent and natural. However, some concerns were identified, such as the robot interrupting other tasks, understanding the status of the robot, and privacy issues. The findings from this study can be used when designing human-robot interactions for multipurpose robots in work contexts and when selecting suitable HFE methods for user-based PoC testing.
The current work-in-progress report explains the development of heuristics to calibrate user-perceived trust in the user interface design of complex systems within a risky socio-technical context, where trust issues may lead to negative consequences. We present results from ongoing research following an adapted methodology commonly used for heuristic development, which includes three phases: exploratory, conceptualisation, and evaluation. However, various approaches are used to collect data for heuristic development; the process of transforming this data into heuristics remains unclear. We address this issue by applying an Activity Theory Model to structure our research activity and synthesise results from the previous research phase, specifically 28 factors influencing the perception of trustworthiness in socio-technical systems (STS) and 17 recommendations with additional evidence from the literature to establish an initial heuristic H1: Ensure Information Transparency, with three sub-heuristics: H1.1 Communicate System Purpose, Processes, and Performance, H1.2 Ensure Quality Information, and H1.3 Support User Cognitive Processes. Future work includes expert evaluation of the heuristics’ clarity, relevance, and completeness, prioritisation through the Analytic Hierarchy Process (AHP), collection of qualitative feedback, and a user study comparing a baseline GUI with a heuristics-guided GUI in an AI-supported clinical scenario.
The problem addressed in this study is the lack of a proper conceptualization of the Distributed Ledger Technology-based Central Bank Digital Currency technical language that guides central banks and their stakeholders in the platforms design and implementation. This research aims to improve the DLT-based CBDC knowledge, proposing a human-centered reference model. We use Design Science Research methodology combined with other research methods. The literature has not provided a reference model that can be used to improve the design of the CBDC system to meet regulatory requirements. This study fills the gap in the literature by presenting a human-centered reference model for DLT-based CBDC that serves as a decision-aid tool to study citizens’ needs and behaviors, to design user experience, to improve alignment between cultural values and policy roles, and to facilitate communication between experts and stakeholders on currency digitalization process.
Human-centred design only inadequately addresses global problems such as climate change, biodiversity loss, and inequality. The paper utilises conceptual design and experience spaces as an explanatory tool to propose adjustments towards a humanity-/life-centred design approach. In particular, two aspects of behavioural change are discussed. First, critical co-reflection and diversity in experience spaces support the reshaping of usage patterns with negative effects. Second, design spaces extended by representations of the context of design help to open up the designer’s choices regarding the production of digital artifacts.
Social media challenges traditional gatekeeping mechanisms in the art market by enabling individuals to create, exhibit, and sell artwork independently of institutions. This paradigm shift holds significant implications for Estonian creatives, who contribute 4.2% to the national economy and increasingly integrate digital technologies into their practices. Established Estonian artists actively use digital platforms to connect with audiences, sometimes provoking public discourse and amplifying the influence of local creatives internationally. This study investigates the motivations, platform preferences, and attitudes of Estonian art practitioners toward social media through 12 semi-structured interviews with musicians, performative artists, designers, visual artists, and art researchers. Thematic analysis reveals active social media usage, with Facebook and Instagram being the most popular platforms, while TikTok remains underutilized. Key uses include content sharing, professional observation, and event organization. The findings highlight the dual role of social media as a complementary tool and a disruptive force in artistic practices, while also surfacing emerging concerns around digital ergonomics—such as cognitive load, attention management, and the sustainability of always-on engagement for creative well-being.
This paper presents a case study usability evaluation of a graphical user interface (GUI) used in the Virtual Terrain Image Generation System (VTIGS) developed by AMST Systemtechnik GmbH. The GUI is used by instructors to configure night vision training scenarios for pilots using Night Vision Goggles (NVG). However, many instructors are non-aviation professionals. This study examines how the perceived complexity of the GUI differs between aviation and non-aviation users. Using mobile eye tracking glasses (VPS 19 from Viewpointsystem GmbH), think-aloud protocols, and post-task surveys, it shows differences in interaction patterns and attention distribution. This work builds on findings from the author’s master’s thesis but is presented here as an independent summary.
The technological advancement of artificial intelligence (AI) and large language models (LLMs) are rapidly changing what systems can do and how people interact with them. Usable and Explainable AI (XAI) is identified as a key human-centred AI (HCAI) challenge to address. Although existing research offers high-level principles and guidelines to design for AI, there is limited support in how to translate these into interface decisions, specifically for the design of explainable user interfaces (XUI). This research aims to address the gap through the development of a practical framework for user experience (UX) designers. Through the RtD methodology, the research follows a qualitative approach across four phases. In Phase 1, a scoping review and thematic analysis identified interface-level XUI guidelines. In Phase 2, the guidelines were validated and operationalised into practitioner-facing reflective design questions through two expert reviews. In Phase 3, a participatory workshop with UX designers classified UI patterns across explanation dimensions to support explainability. Finally the outputs across all phases were synthesised into a practical and flexible framework for UX designers. A set of 5 learning cards introduce the theoretical foundations, 14 XUI guidelines accompanied by reflective questions support theory in practice, and a UI pattern decision tree to guide the selection of design patterns. The result is a design artefact that bridges academic theory and design practice for the design of XUIs in AI products and systems.
Digital behaviour change intervention systems often rely on static user models or opaque black-box models, limiting their ability to adapt to real-time psychosocial fluctuations. Such intervention systems can result in poorly timed or misaligned interventions across various contexts. To address this, we propose a dynamic user modelling system design that leverages continuous wearable and contextual data to infer psychosocial factors and map them to relevant behaviour change interventions as they evolvg. The design integrates multi-label deep learning with explainable AI techniques (SHAP, LIME) to ensure transparency and interpretability. The system advances existing methods by dynamically responding to real-time psychosocial changes, offering interpretable predictions, and enabling precise, theory-grounded intervention delivery — capabilities that static or black-box systems lack.
The rise of the subscription economy has introduced new challenges for user experience, particularly around service cancellation processes. This study examines the presence of dark patterns, also known as deceptive interface designs, in digital subscription service cancellations and their impact on user experience and willingness to engage in additional services. A scoping review of 28 academic and industry sources was conducted, resulting in a taxonomy of 44 dark patterns categorised into ten thematic groups. This taxonomy was applied in a comparative analysis of six real-world subscription services, identifying the most common patterns to obstruct cancellation. A vignette-based survey experiment was conducted to evaluate the effects of dark patterns on user experience. Results showed a 28% reduction in user trust (p < 0.001) and a 54% decrease in usability scores when participants were exposed to dark-pattern-heavy cancellation flows, compared to transparent alternatives. The findings demonstrate that dark patterns undermine trust, usability, and user retention intentions. This research presents a taxonomy specific to dark patterns in subscription cancellation processes, identifies deceptive strategies, and highlights the ethical implications for digital subscription service cancellation design. The study advocates for the adoption of transparent, user-centred cancellation practices. It provides a foundation for future research examining the evolving landscape of dark patterns in adaptive and AI-generated interfaces.
This poster paper presents the design and development process of TacitFlow, a voice-controlled assistant intended to support the transfer of tacit knowledge in high-stress public sector environments. Built using a Design-Based Research (DBR) approach, the system integrates voice input, knowledge graphs (KGs), Graph-Based Retrieval-Augmented Generation (GraphRAG), and graph-of-thought (GoT) Artificial Intelligence (AI) reasoning for contextual support. The system enables knowledge capture, question-answering, and peer learning through a mobile-first interface. This paper outlines the core functionality, architecture, and future evaluation plan. The design is grounded in findings from a scoping review and interviews, which are described in a separate Work-in-Progress (WiP) paper titled: "Designing for the Unspoken: A Work-in-Progress on Tacit Knowledge Transfer in High-Stress Public Institutions" in ECCE 2025 proceedings.
The convergence of Artificial Intelligence (AI) and Extended Reality (XR) transforms how we learn, connect, and experience ourselves and others. Immersive environments intensify presence and connection while introducing layered cognitive and affective demands that call for new perspectives on interactive technologies, in particular from a design and ethics point of view. The keynote explores these issues through surveying studies and experiments using different methods from ethnographic studies to controlled experiments to design explorations. Ethnographic studies of Social VR investigate new cultures and practices such as phantom touch, users mute by choice, mirror dwelling, and VR drinking. Controlled experiments using neurophysiological measures investigate emotions in avatar interaction by studying tactile perception, decision-making, empathy, and physical responses through subtle affective cues. Finally, design explorations utilizing biofeedback and neuroadaptivity to study neurophysiology-driven meditation, dyadic biofeedback, and intentional time control. By interweaving theory and participant voices, we argue for immersive tailoring as a strategy to return agency to users while mitigating the risks of AI-driven XR systems, framing pathways toward responsible, human-centered XR futures.
Abstract: The panel on the present and future of cognitive ergonomics in Europe will explore the views of leading figures in cognitive ergonomics in Europe, in the context of a critical reflection for a better tomorrow. The panel’s origin is from an interactive event at ECCE-2024 in Paris and uses the findings from that discussion to position and contextual the goals and ambitions for this panel, setting out key objectives to be discussed. The panel will then discuss and debate the position of cognitive ergonomics in today and tomorrow’s world.
