Veranstaltungsprogramm

"Gaming sickness, where players experience dizziness, nausea, or even vomiting while playing video games, is typically viewed through a quantitative lens, limiting our insights into the lived experience of the phenomenon and thus concealing mitigation strategies. To gain a more nuanced understanding of gaming sickness, and to explore possible lanes of future research, we conducted twelve semi-structured interviews with people affected by gaming sickness.

Our results show that the experience of gaming sickness is highly individual, with a wide spectrum of symptoms and varying severity. Environmental factors can influence gaming sickness, and many participants have developed individual contextual and game-related coping strategies, which they apply with varying degrees of success. For games to be more accessible to everyone, future work should focus on studying specific triggers, and needs to explore different forms of adjustments so players can tailor games to their individual needs."

Due to changing demographics, limited availability of experts, and frequent job transitions, retaining and sharing knowledge within organizations is crucial. While many learning systems already address this issue, they typically lack automation and scalability in teaching novices and, thus, hinder the learning processes within organizations. Recent research emphasizes the capability of explainable artificial intelligence (XAI) to make black-box artificial intelligence systems interpretable for decision-makers. This work explores the potential of using (X)AI-based learning systems for providing learning examples and explanations to novices. In an exploratory study, we evaluate novices' learning performance in a learning setting taking into account their cognitive abilities. Our results show that novices increase their learning performance throughout the exploratory study. These results shed light on how XAI can facilitate learning, taking first steps towards understanding the potential of XAI in learning systems.

Understanding user behavior is at the heart of user interface design, but can only be quantified to some extent. Qualitatively analyzing individual usage sessions is especially important in open-ended tasks like literature search. In this paper, we present a visual representation of logging data that provides the basis for an in-depth analysis and annotation of search and exploration sessions. The visualization was developed to evaluate the citation-based literature discovery tool PUREsuggest and is thus aimed at visualizing logging data of a literature search system. Events are represented on a timeline in different layers as bars and icon-based glyphs, and contextualized by the additional visualization of item states and active user-set search modifiers such as filters or keywords throughout the session. We demonstrate the applicability of the visualization by evaluating excerpts of two user sessions as an example.

AI-based tools such as ChatGPT are increasingly being used in education. However, many students and teachers are unsure whether, how, and to what extent they are allowed to use these tools in higher education. Overall, there is a lack of guidelines on using AI-based tools at German universities. Therefore, this study conducts a comparative analysis of existing guidelines to extract and aggregate the most important recommendations for use. The results show the high relevance of guidelines and the need for clarification regarding the conditions under which AI-based tools are considered assistive, the responsibilities of the stakeholders, and how risks and challenges can be addressed to ensure e.g., academic integrity. The work results support the synthesis of guidelines in higher education.

This study explores the acceptance of augmented reality (AR) headmounted displays (HMDs) in enhancing support for the crafts sector. In a qualitative field study with three craft companies from different sectors, it was found that AR holds the potential to improve time efficiency, task learnability, and hands-free operations, fostering better problem-solving and information change. The framework for data analysis was based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) model. However, we also found that challenges such as the fragility, bulkiness and cost of current HMDs, along with characteristics of the crafts sectors such as widely changing locations such as lighting and spatial conditions, hinder widespread adoption. The study highlights the need to enhance factors that promote acceptance and address those that inhibit it.

This paper investigates the integration of motion capture and virtual reality (VR) technologies in competitive ballroom dancing (slow walz, tango, slow foxtrott, viennese waltz, quickstep), aiming to analyze posture correctness and provide feedback to dancers for posture enhancement. Through qualitative interviews, the study identifies specific requirements and gathers insights into potentially helpful feedback mechanisms. Using Unity and motion capture technology, we implemented a prototype system featuring real-time visual cues for posture correction and a replay function for analysis. A validation study with competitive ballroom dancers reveals generally positive feedback on the system’s usefulness, though challenges like cable obstruction and bad usability of the user interface are noted. Insights from participants inform future refinements, emphasizing the need for precise feedback, cable-free movement, and user-friendly interfaces. While the program is promising for ballroom dance training, further research is needed to evaluate the system’s overall efficacy.

Deciding where to work in hybrid environments involves various factors beyond work-life balance. Awareness of colleagues’ tasks arises from overheard conversations, screen glimpses, and other informal exchanges in the ofce. This knowledge sharing is crucial for efective work. However, knowledge workers navigate between the ofce, home, and mobile work, leading to constant re-negotiations of hybrid work environments. This article uses data from ethnographic observations and qualitative interviews to illustrate how knowledge workers outweigh decisions for work location and strategically use informal ofce interactions to advance tasks on-site. Although the body of research on awareness and knowledge work is by now substantial, the spontaneous knowledge exchange in everyday practices is under-explored. This study addresses two questions: (I) what factors infuence decisions between mobile and ofce work, and (II) how on-site practices generate diferent types of awareness that support task progress.

Ensuring optimal care post-hospitalization is a significant challenge for healthcare systems. Discharge management (DM) is crucial for continuing care, yet process-related issues persist. Artificial Intelligence (AI)-supported systems may address DM-related issues, but research on the needs of hospital staff is limited. This paper presents results from the first phase of a multicenter project aimed at developing an AI-supported system to predict aftercare needs and improve DM processes in German hospitals. We conducted an exploratory needs analysis using participatory methods (workshops, questionnaires and interviews) and defined suitable use cases. We observed a high level of interest in the proposed AI-supported system. However, participants expressed doubts about the effective implementation due to the current state of their hospital’s digital infrastructure. The resulting use cases focused on the reception, processing and interpretation of "plausible" data. These outcomes form the basis for the further research and development with hospital staff and external developers.

Time management has the potential to maintain the learning and working productivity. Prominent techniques—such as Pomodoro—typically suggest to alternate productive periods and breaks. They are mostly time-based and lack adaptability to individual preferences and cognitive workloads. In the context of learning, this leads to suboptimal learning experiences, with rigid time structures hindering productivity and reducing efficiency. We introduce CoLoTiMa, a novel approach that dynamically adjusts learning period durations. It integrates real-time cognitive-load measurements and user self-assessment to tailor learning experiences. Through the use of eye-tracking, CoLoTiMa optimises the duration of learning blocks in accordance with individual learning preferences and thus fosters personalised and efficient outcomes.

Transitioning seamlessly from the real world into the digital world through the mixed reality continuum remains challenging. This paper investigates transitional design principles across the MR spectrum, anchored by a review of "The MagicBook'', a pioneering work that introduced the concept of transitional interfaces to the HCI community. Employing a forward-backward method, we reviewed 309 publications to understand the landscape of MR transitions. Our analysis outlines four distinct transition types within MR environments, offering a novel classification scheme. From this literature corpus, we identify four categories, setting a foundation for transitional interfaces UX evaluation.

This paper explores the personalization of wake words in Active Noise Cancelling (ANC) enabled headphones designed as assistive technology for noise-sensitive individuals. We focus on the incorporation and assessment of intended wake words, categorizing them based on fillers and first names. Given the language-dependent nature of this approach, our research primarily addresses the English language. We propose an evaluation mechanism to determine the efficacy of potential wake words, predicting their recognition accuracy and length. The study involves a comparative analysis between combinations of fillers and first names, aiming to identify the optimal pairing. We offer recommendations on the best-performing combinations, enhancing the reliability and user experience of ANC headphones as a supportive tool for noise-sensitive individuals. Our findings aim to provide a robust framework for developing highly responsive and personalized wake word systems, tailored to the unique needs of noise-sensitive individuals in office environments.

This study validates Lorenz’s Kindchenschema features by using a commercial text-to-image generator (Midjourney v.5.2) to generate stimulus material that depicted either animals or objects with or without the text prompts for "cute"' and "anthropomorphic". In an online study human participants evaluated the presence of Kindchenschema features in said images. Our results show their increased presence and perception in AI-generated images created with the prompt "cute" compared to those explicitly excluding it, as well as the systematically prioritisation of features such as "large, deep-set eye" and "rounded body shapes" in cute images. Whereas "round, protruding cheeks", traditionally considered essential by Lorenz, ranks lower. Furthermore, the anthropomorphisation of subjects reduces the likelihood of Kindchenschema features, highlighting constraints in AI-generated stimuli. This research demonstrates the potential of AI-tools to objectively quantify and test theoretical concepts as represented in popular culture.

Design features of social media platforms, such as infinite scroll, increase users’ likelihood of experiencing normative dissociation — a mental state of absorption that diminishes self-awareness and disrupts memory. This paper investigates how adding design frictions into the interface of a social media platform reduce mindless scrolling and user satisfaction. We conducted a study with 30 participants and compared their memory recognition of posts in two scenarios: one where participants had to react to each post to access further content and another using an infinite scroll design. Participants who used the design frictions interface exhibited significantly better content recall, although a majority of participants found the interface frustrating. We discuss design recommendations and scenarios where adding design frictions to social media platforms can be beneficial.

Emotion regulation is essential for students’ well-being and success. However, many students lack emotion regulation skills. This study explores the use of smartwatches to deliver real-time emotion reg- ulation interventions supporting students in stress-full situations. Given their ability to monitor physiological indicators and provide immediate feedback, smartwatches offer a promising solution for delivering emotion regulation micro-interventions. We designed a smartwatch-based micro-intervention and conducted a usability test to evaluate how students interact with the interventions on the smartwatch and their potential to support regulating emotions during stressful situations. Within a controlled laboratory envi- ronment, the study involved 13 participants (7 males, 6 females) who were exposed to positive and negative mood induction tasks, followed by emotion regulation interventions delivered via smart- watch notifications. Usability and user experience were assessed using the System Usability Scale (SUS) and the User Experience Questionnaire (UEQ), complemented by qualitative interviews. Our findings provide insights into the potential of wearable technology to support emotion regulation through micro-interventions. How- ever, future research is needed to enhance personalization, usability, and real-world applicability of the proposed smartwatch-based in- tervention to successfully support emotion regulation.

The use of digital technologies and media seems essential to ensure social participation. In order to apply them in a reflective, self-determined and responsible manner, regardless of age, it is not only crucial to be familiar with their applications, but also to understand how they work technically and to competently assess their socio-cultural effects. These requirements have already been outlined in the Dagstuhl Declaration, and the digital skills to be acquired have been specified in the competency framework of the European Union and the strategy of the Standing Conference of the Ministers of Education and Cultural Affairs of the Federal States in Germany. However, unlike in school education, developing appropriate education resources in cross-age and adult education has received little attention. Motivated by this, the work presents an analog-digital escape game for the tangible teaching of digital skills. An initial empirical study indicates that it is suitable for practical use in schools and out-of-school settings and can contribute to the age-independent acquisition of fundamental digital skills as a basis for enhancing digital sovereignty.

AI-generated journalism (robot journalism) enables the automated creation of news articles through Artificial Intelligence (AI). Especially in sports reporting robot journalism enables providers to publish standardized match reports quickly after sporting events (e.g. soccer games). This study examines the influence of disclosing the type of origin (human and AI) on the perception of the credibility of sports reporting. For this purpose, an quantitative online survey was conducted with 154 participants, where two match reports about the same soccer game were compared: One of these reports was written by a journalist, while the other was AI-generated. The participants were divided into three groups, with varying disclosures on the type of origin (no disclosure, correct disclosure, manipulated disclosure). The analysis showed that the origin disclosures had no significant influence on credibility. Both expertise and trustworthiness were rated similarly. Since readers are indifferent about the source of information, this suggests that the use of AI in sports reporting can be useful to increase efficiency. However, in a wider sense, this indifference poses challenges to policymakers trying to contain the spread of misinformation and fake news based on the use of AI.

"The increasing popularity of analytics-driven design questions the

role of direct user feedback to improve User Experience (UX). In

this study, we frst deployed an interaction tracking-tool on ex-

isting software to inform design decisions in an analytics-driven

prototype. Second, we collected direct user feedback on this ver-

sion and iterated the design, resulting in the feedback-prototype. A

meta-evaluation study comparing the UX of both prototypes and

the existing software revealed that, the analytics-driven prototype

was rated signifcantly higher on the UEQ-S questionnaire than

the original software. Surprisingly, there were no signifcant im-

provements in perceived UX between the analytics-prototype and

the feedback-prototype despite a reduction of required actions. We

conclude, that UX can be signifcantly enhanced through behavior

analytics alone. While diferences between the two prototypes were

little, relevant notions can only be retrieved directly from users.

Future work needs to replicate these fndings with independently

developed, more diverse prototypes."

Artificial Intelligence (AI) education poses challenges for business students with limited technical knowledge. This exploratory study examines the use of Extended Reality (XR) technologies to improve AI learning. Twelve business students tested three XR applications — mobile Augmented Reality (AR), headset AR, and headset Virtual Reality (VR) — focusing on AI concepts such as k-means clustering and neural networks. Focus group discussions revealed that XR technologies provide immersive, interactive experiences that enhance understanding of complex AI concepts. Students appreciated the hands-on interactivity, though preferences varied between VR and AR. Well-known issues such as motion sickness and accessibility were noted. Our findings suggest integrating XR with traditional methods to create a more effective AI learning approach, highlighting XR's potential to equip business students with essential AI skills.

Cardiac rehabilitation (CR) is crucial for secondary prevention of cardiovascular diseases. Yet, many patients with a clear indication of CR do not participate in such programs. To build a more nuanced foundation for developing digital health technologies supporting patients’ CR uptake, we explored the practiced CR referral process to identify context-specific barriers in Austria. We conducted a contextual inquiry including four observations in both out- and inpatient settings and 13 stakeholder interviews. The results indicate a lack of a defined referral process, limited intersectoral communication, doctors’ unawareness and knowledge gaps, and patients missing clear information about CR. Thus, digital health technologies that aim to support patient-centered cardiovascular care pathways should be designed for timely patient information and engagement, automated referral support, and enhanced intersectoral communication.

This paper explores the use of semantic differentials in evaluating interactive products from the user's perspective, focusing on children and adolescents. While semantic differentials have been widely utilized in assessing user experience (UX) among adults, their application to younger target groups remains underexplored. Existing studies develop and validate questionnaires with semantic differentials for assessing UX in children, but challenges remain in understanding how adolescents evaluate products using this method and identifying potential error sources. The study aims to gain a comprehensive understanding of how children and adolescents interpret semantic differentials in product evaluation, particularly in usability testing scenarios. The study shows that children and adolescents' comprehension of semantic differentials in UX questionnaires is affected by product characteristics and individual usage context, not just vocabulary. Analysis across class levels and product types identified error sources in interpreting semantic differentials for UX testing. Involving young individuals in shaping or selecting UX dimensions for questionnaires is crucial for ensuring their relevance and effectiveness.

Dementia is one the leading causes of death globally. Although currently no cure exists, there are multiple interventions aimed at improving the quality of life of people with dementia by leveraging elements of cognitive stimulation therapy. This work is the first of a several-year research agenda that investigates the feasibility and effectiveness of embodied voice assistants to deliver such interventions. We describe the development of the GRACE prototype based on an open-source social robotics platform. We expand its functionality by adding two intervention components of cognitive stimulation therapy and making the robot more interactive by integrating a public large language model. We also conduct a first evaluation with 11 healthy participants who assess perceived technology characteristics including working alliance, an important relationship quality, which is robustly linked to treatment success. Results indicate that GRACE was perceived positively. We conclude this short paper with suggestions for improvement and outline future work.

Extended Reality (XR) has already enabled sophisticated implementations of immersive visualizations, providing a more intuitive and engaging way of analyzing data. Yet, the user interaction with such immersive visualizations remains challenging, often relying on hand tracking or additional devices. We introduce a novel XR prototype that leverages the concept of embodied exploration, allowing users to interact with an exemplary visualization directly through their spatial position within the room relative to the displayed data. This approach eliminates the need for handheld controllers, offering a more intuitive engagement with the visualization. Our preliminary evaluation with twelve participants reveals a general preference for using XR for immersive visualizations compared to PC and paper-based versions. We suggest further research into non-standard interaction and exploration modalities for data analysis applications using XR, potentially offering new possibilities for engaging interactions with data.

As Artificial Intelligence (AI) technology is becoming widespread, small to medium sized enterprises (SMEs) are beginning to use it extensively. This paper presents the results of an interview study with eight CEOs or co-founders of SMEs. We explore the practical applications of AI technologies within these SMEs and their anticipation of forthcoming European AI regulations, specifically the AI Act. Additionally, we also investigate attitudes and dispositions towards voluntary codes of conduct as outlined within it. This study aims to shed light on the operational, regulatory, and ethical dimensions of AI integration within SMEs. It reveals that SMEs favor third-party AI systems, particularly those based on Large Language Models (LLMs), due to their convenience and minimal integration effort. Additionally, SMEs are keenly aware of their need for external support to navigate upcoming AI regulations, emphasizing the importance of tailored interventions to ensure compliance and optimal use of AI technologies. Lastly, SMEs view voluntary codes of conduct as outlined in the AI Act as a testament to a company’s commitment to go beyond mere legal compliance, thus reinforcing trust. Based on our findings, we propose three design implications for the HCI community: convenient AI integration, post-adoptive regulatory support, and proactive ethical design.

Telepresence robots offer great opportunities for children with long-term illnesses to continue attending school. Consequently, they are already used if children are absent for long periods. When designing such systems, the privacy of various stakeholders must be considered. However, conflicts often arise because the privacy requirements of different user groups cannot be fulfilled simultaneously. In this paper, we analyze the corresponding trade-offs that have to be made when designing telepresence robots under conflicting privacy requirements. We analyzed previous literature and held three workshops with different user groups (children, parents, teachers, head teachers, media educators, and supporting personnel) with and without experience with telepresence robots in schools. Based on the literature and the workshop results, we present four major privacy trade-offs we identified and discuss design approaches for them. With this work, we contribute to the design research on telepresence robots in schools by revealing the major privacy-related conflicts and potential design approaches to overcome the conflicts.

Ethics education in data science aims to teach aspiring data scientists a critical reflective data science practice. However, university courses must bridge the gap between theoretic knowledge of ethics and ethical practice. Towards this, our research aims to understand how we can promote a critical reflective practice through reflection. We, therefore, investigate how data science students start reflecting and what constitutes reflection-triggering contexts in data science education. For this, we introduce a reflective essay assignment and propose a reflection-sensitive inductive content analysis to analyze it. Our findings based on seven student reflective essays suggest that important reflection trigger characteristics in data science ethics education include students’ expectations, a new insight, motivators for reflection related to expectations, teaching formats, and emotions. Our reflection-sensitive analysis is suitable for explorative analysis and creates transparency about existing sensitizing concepts.

We ask whether users should adjust to AI systems or vice versa. Levels of automation (LOAs) are task dependent, may vary within one task, and also may change over time. People’s diverse abilities and preferences make the usage of AI systems possibly personal. Automation design is a complicated task. We investigate varying levels of LOAs in one specific decision-making process. For this, we conduct an experiment, where n=24 volunteers participate in a within-subject face-recognition experiment. Face-recognition is an innate ability mastered by humans. Reason are specialized neurological systems. This also makes it an intuitive task. The results show that of the five tested LOAs, each one leads to personal best and personal worst decisions regarding accuracy and time. Similarly, each LOA is preferred or opposed by participants. This shows, that there is no “one-size-fits-all” LOA, suggesting that careful design is required and multiple LOAs should be offered for a task.

The visual appearance of avatars can influence users' behavior and perception based on their stereotypical associations. Avatars' clothing govern the visual appearance and is not only aesthetically important but also determines the anticipated abilities. Therefore, avatar designers need to know the abilities that are associated with the clothing. However, a systematic investigation of avatars' clothes and the perceived abilities is missing. Consequently, we conducted an online survey with 61 participants to explore the functionality, physical and cognitive abilities, as well as comfort and aesthetics of 15 selected popular action video game characters' clothes. We contribute with a heatmap illustrating the correlation between clothes and specific abilities. This heatmap serves as a tool for avatar designers during the creation process to anticipate the perceived abilities of their avatars' clothes.

The rapid advancement of language models is driving significant developments in voice assistants, enabling tasks to be performed without graphical or tactile user interfaces and fundamentally changing human-machine interaction. This article explores interaction with intelligent voice assistants (IVA) to improve usability and user experience. The focus is on designing the interaction between question and answer in the response time. The study demonstrates the effectiveness of the role-playing method in testing machine interactions in an accessible way, leading to the development of sound interactions. Furthermore, the study shows that the latency of a response can be divided into three distinct phases that can be deliberately designed. Additional findings indicate that the use of acoustic feedback and clear signals reduces uncertainty and enhances perceived response time.

This paper investigates the factors and platform-specific affordances that contribute to the popularity of mental health (MH) content on TikTok, a topic of growing importance within Human-Computer Interaction (HCI) due to its impact on public MH literacy and individual well-being. Diverging from studies that focus predominantly on the "For You Page" algorithm, this research employs qualitative methods to explore a broader array of features that facilitate the platform's success in MH discussions. Through semi-structured interviews with fourteen TikTok users, including viewers and creators, this study examines how TikTok's community, algorithmic recommendations, interactive features, and engaging environment support the sharing and consumption of MH content. Additionally, it explores user perceptions and attitudes towards TikTok as an MH platform, revealing various opinions, impacts, and experiences. Our findings highlight how TikTok fosters a unique space for MH discussions and provide design considerations for optimizing such platforms for MH communication.

Many companies face the challenge of identifying and effectively implementing a suitable UX process that meets their individual requirements and organisational contexts. This article analyses which stakeholders in companies significantly influence the selection and implementation of UX processes in the development of digital products. Based on an online survey, the degree of involvement of various stakeholders is analysed. The study shows that UX and design specialists in particular, as well as product management, play a central role. It also shows that many companies favour flexible UX processes and prefer internal expertise. The research emphasises the need for practice-oriented frameworks and tools to support the decision-making and implementation of UX processes. The insights gained are to be incorporated into the development of a solution that is customised to the specific needs and UX maturity levels of companies in order to enable more effective UX integration.

The COVID-19 pandemic has accelerated hybrid working models and required new approaches to leadership and collaboration. This study refects on a series of shorter stints of ethnographic feldwork spanning over 14 months exploring the practical implications of hybrid work environments in SMEs. The primary focus of this paper is on the challenges encountered in accessing the research feld during the ethnographic research. Our study highlights the challenges and opportunities that arise in the design and practical application of research in hybrid work environments in SMEs. It demonstrates that hybrid working conditions require fexible and adaptable research methods to capture the complex realities of modern work practices. Despite access issues and confdentiality concerns, the ethnographic approach provided valuable insights into the practices and structures of hybrid teams. This paper aims to stimulate discussion on efective research designs and strategies to support hybrid work environments and contribute to HCI research. By fostering a deeper understanding of the dynamics and challenges of hybrid teams in SMEs, with an emphasis on the strategies for accessing these organizations.

As AI becomes more powerful, it also becomes more complex. Traditionally, eXplainable AI (XAI) is used to make these models more transparent and interpretable to decision-makers. However, research shows that decision-makers can lack the ability to properly interpret XAI techniques. Large language models (LLMs) offer a solution to this challenge by providing natural language text in combination with XAI techniques to provide more understandable explanations. However, previous work has only explored this approach for inherently interpretable models--an understanding of how LLMs can assist decision-makers when using deep learning models is lacking. To fill this gap, we investigate how different augmentation strategies of LLMs assist decision-makers in interacting with deep learning models. We evaluate the satisfaction and preferences of decision-makers through a user study. Overall, our results provide first insights into how LLMs support decision-makers in interacting with deep learning models and open future avenues to continue this endeavor.

Movies leave a personal impression with the viewers, which they might be interested to express or share. Going beyond textual reviews and diary entries, we suggest an interface, called MoViQuotes, to capture more personal and subjective impressions of movies that can be shared with others. Through selections of visualized characteristic

colors and textual quotes, users express the perceived moods and vibes of the movie. The system then generates variants of visual quotes that embed a generated image into a stylized template to illustrate the selected quote using the chosen colors.

Angesichts des dringenden Bedarfs, nachhaltiges Verhalten in der Gesellschaft zu fördern, gewinnen Technologien zur Unterstützung positiver Verhaltensänderungen an Bedeutung. Generation Z, bekannt für ihre technologische Affinität und ihr Umweltbewusstsein, steht im Mittelpunkt unserer Betrachtung. Unser Paper berichtete den Einsatz persuasiver Computing-Strategien zur Förderung ökologischer Nachhaltigkeit innerhalb dieser Generation. Im Rahmen einer Lehrveranstaltung zu User Experience für mobile Endgeräte entwickelten Studierende der Generation Z App- und haptische Prototypen anhand von Anforderungen ihrer Altersgenoss:innen. Interviews und Evaluationen zeigten Gründe für Zurückhaltung gegenüber umweltfreundlichen Lebensstilen und lieferten Implikationen für das Design persuasiver Technologien. Die Ergebnisse bieten erste Einblicke in die Gestaltung von Technologien, die Generation Z für Umwelt- und Nachhaltigkeitsthemen mobilisieren können.

Monitoring drivers’ health is crucial for saving lives in emergencies and enabling in-car health applications. The state of the art in pulse monitoring is contact-based sensors which impair the driving expe- rience and have to be applied manually before driving. This paper focuses on automated hyper parameter optimizing the Eulerian Video Magnification (EVM) algorithm, which detects heart rates through non-contact facial camera images, for use in driving sce- narios. We conducted a user study where 21 participants performed a driving simulation while their heart rates were recorded by a wearable fitness tracker (serving as ground truth) and facial im- ages with an RGB camera. Our findings indicate that, despite using the optuna library for hyper parameter tuning, the Eulerian Video Magnification algorithm is insufficient for accurate pulse detection in a driving simulator environment.

"To help ensure children do not suffer when parents are overwhelmed, many countries, including Germany, offer child protective services and family support programs. In Germany, family centers on district-level provide preventative support services. However, the families most in need of their services often face the greatest barriers to accessing these, a situation known as the “prevention dilemma.”

A human-centered design process was undertaken, involving family centers and target audiences, to identify and address these barriers. The resulting native app aims to improve accessibility to support services by providing essential information.

The evaluation shows the app has a high usability, was well-received by the target audience, and has the potential to mitigate some of the access barriers. This study highlights the importance of accessible support measures for vulnerable families, contributing to improved child welfare."

Menstruating individuals experience various physiological and psychological changes throughout their reproductive lives and each menstrual cycle (MC). Although numerous digital health technologies (DHTs), such as menstrual health (MH) tracking apps, exist, there is limited research on how customization options and personalized visualizations of MC data and associated symptoms impact user experiences with these technologies. Furthermore, evidence on the long-term effects of these features on MH awareness and literacy is sparse. Therefore, the goals of this research are twofold: First, we examine users’ current experiences and needs regarding MH (technologies) in an online survey with 272 participants, highlighting a discrepancy between individuals’ perceptions and their actual understanding of their MH and its impact on their well-being. Yet, participants showed a strong interest in understanding and managing their MH through DHTs. Desired improvements in MH technologies include more accurate prediction of MC phases, as well as more comprehensive insights and support in managing MH through customization and personalization. Second, to examine how customization options and personalized visualizations of MH data affect user engagement and improve MH awareness and literacy in a longitudinal study with up to 70 participants, we are currently developing the MH study app CyMe [si:][mi:].

Robots are expected to be integrated into human workspaces, which makes the development of effective and intuitive interaction crucial. While vision-and speech-based robot interfaces have been well studied, direct physical interaction has been less explored. However, HCI research has shown that direct manipulation interfaces provide more intuitive and satisfying user experiences, compared to other interaction modes. This work examines how built-in force/torque sensors in robots can facilitate direct manipulation through nudge-based interactions. We conducted a user study (𝑁 = 23) to compare this haptic approach with traditional touchscreen interfaces, focusing on workload, user experience, and usability. Our results show that haptic interactions are more engaging and intuitive but also more physically demanding compared to touchscreen interaction. These findings have implications for the design of physical human-robot interaction interfaces. Given the benefits of physical interaction highlighted in our study, we recommend that designers incorporate this interaction method for human-robot interaction, especially at close quarters.

Map-based web applications are often used to display spatial information in the context of (open) data from the environmental and urban infrastructure sectors. As the number of thematic layers displayed on these applications grows, maintaining user interface intuitiveness becomes increasingly challenging. To provide an initial account of design practices in the area, this article examines 19 map-based applications with multiple thematic layers, along four dimensions: thematic content complexity, functionalities for layer search, legend design and functionalities for the search of location information. We then report on findings from a brainwriting workshop involving 11 participants, during which three applications were used to identify design challenges and opportunities. The observations and design issues presented are relevant to researchers and designers of applications displaying multi-theme spatial information.

Cybersickness (CS), a visually induced motion sickness causing unwanted symptoms like nausea, disorientation, and vertigo, remains a significant issue in Head-Mounted-Display (HMD) based virtual environments, hindering the mainstream adoption of Virtual Reality (VR). The exact cause of cybersickness is still not conclusively clarified, however the predominant theory of sensory conflict suggests that the discrepancy between perceived and expected visual, vestibular, and proprioceptive information triggers these symptoms. Mitigating sensory conflict with the help of supplementary motion simulation aligning visual and vestibular stimuli appears promising, however to which extent motion has to be simulated is still underexplored. This work presents the results of a study (n = 43), based on a between-participants design, investigating the effects of minimal rotational motion (MIROMO) cues for pitch and roll (+/- 6°) on cybersickness in a lean-back VR flight experience. CS was measured with both, self-reported questionnaires, particularly the widespread Simulator Sickness Questionnaire, and biomarkers (electrodermal activity and heart rate variability). The results imply the non-effectiveness of MIROMO cues on cybersickness and thus can help future research experimenting with motion simulation to reduce sensory conflict or to enhance the overall experience. The results contribute to better understand the relationship between cybersickness and continuous locomotion techniques.

German small and medium-sized enterprises (SMEs) are considered at risk of falling behind in digitalization. As SMEs are crucial to the German economy, there have been many recent government digitalization initiatives tailored to SMEs. Survey data from the digitalization project "Mittelstand-4.0 Competence Center Usability" was analyzed to determine whether SMEs and large enterprises (LEs) differ in their interests, needs, knowledge, and implementation of usability, user experience (UX), and related topics, and to examine whether it is justified to limit digitalization programs to SMEs. The analysis revealed only minor differences: Agile approaches and the future of work are more prevalent in LEs than in SMEs. Furthermore, LEs were more likely to use events, in-house training and e-learning on usability and UX. Overall, however, the results indicate that SMEs and LEs have similar usability and UX interests and needs suggesting that both can benefit from usability and UX programs to drive digitalization.

Collaborative robot (cobot) systems are expected to be easy to operate. However, reacting to an error message may not be as intuitive as operating the cobot. This paper explores the integration of a multi-modal conversational agent (text, touch and voice) into a robotic system to improve the robot-user interaction during error handling. The conversational agent enables text- and voice-based interactions to improve user experience and efficiency. We evaluate a prototype in a user test with experts and novices in order to assess its effectiveness, usability, and impact on user-robot interaction and show that conversational agents can enhance collaboration between robots and users, particularly benefiting novices.

Balance board training is a promising method to enhance physical rehabilitation for humans with motor problems through interactive exercises. Previous work highlighted the benefits of balance board training in virtual reality (VR) compared to conventional methods. However, it is still unclear how visual target feedback and the presence of an avatar influence balance behavior in immersive environments. We conducted an experimental user study with 24 participants without motor impairments to investigate the effects of visual target feedback and a human avatar on balance performance and perceived workload in VR. Quantitative results show that visual target feedback significantly improves balance performance without increasing workload in VR. In contrast, an avatar shows no effect on performance and workload, which is also confirmed by qualitative feedback. Finally, we discuss the implications of our study for future developments of virtual balance board training exercises and highlight potential applications of visual target feedback.

Chatbots are increasingly used in various domains, but since misunderstandings nonetheless occur, robust strategies are required to handle these misunderstandings and ensure smooth interactions. The evaluation of a faulty chatbot can depend on various factors, for example the task domain or the characteristics of the user. This short paper provides an overview of three different repair strategies for chatbots, taking into account the users’ affinity for technology. The conducted experimental psychological study investigates these three strategies, i.e. the requests for rephrasing, offering suggestions of what might be meant, and politeness to handle conversational errors. Results indicate that providing suggestions has a positive influence on the perceived Accuracy and Likability of the chatbot. Additionally, it seems that the combination of repair strategies should also be considered. Overall, this research contributes to a deeper understanding of ways to recover from a misunderstanding and provides guidelines for the implementation of different repair strategies for chatbot interaction.

Elementary school learners regularly use artificial intelligence (AI) technologies like smart assistants. We discuss how young learners can acquire AI literacy skills related to the practical application, technical understanding, and critical appraisal of AI. We conceptu¬alise a hands-on workshop on understanding how computers can be taught through programming and machine learning for learners without prior knowledge. We evaluate participants’ AI literacy quantitatively and qualitatively and find a large variance in interest, understanding, and knowledge in the relatively homogeneous groups. Our findings suggest that elementary school learners can understand basic machine learning concepts and gain ethical considerations, including data quality and bias. Given the importance of early AI education, the workshop concept can be implemented by teachers without formal computer science training, addressing the current limitation in AI education in elementary schools.

Since ChatGPT's introduction, generative artificial intelligence (AI) has significantly influenced the media, technological innovation, and educational discourse. Its increasing importance, especially in academia, necessitates a detailed examination of the impact of AI on higher education, particularly on how it changes teaching and learning processes. This study therefore looks at the factors affecting students' attitudes towards AI technologies in the university setting, with a particular focus on the differences between business and STEM programmes. Using a mixed methods approach, the study combines surveys and interviews to collect data on students' perceptions, attitudes and experiences with generative AI technology in academia. The data collected is analysed both quantitatively and qualitatively to reveal significant trends and insights into the adoption and use of generative AI tools in the university environment. The main objective of the study is to shed light on the determinants that determine the varying degrees of AI adoption in different academic disciplines. The findings have the potential to inform the implementation of educational technology and assist in the development of strategies for the effective integration of generative AI tools to meet the different needs and preferences of students in a range of academic contexts.

The main quality of virtual reality (VR) is its high immersive potential. However, the resulting inability to perceive the real world surroundings can distract users from the application due to the fear of colliding with obstacles or other people. The Collision Anxiety Questionnaire (CAQ) by Ring et al. measures this fear of colliding and consists of ten items in three subscales: general collision anxiety, orientation, and interpersonal collision anxiety. In this paper, we present a validated German version of the CAQ, which showed a good model fit, including good reliability and internal and discriminant validity. By validating the questionnaire, we offer researchers a tool to further explore and evaluate the concept of collision anxiety, helping to uncover previously under-explored aspects of the user experience.

Digital participation formats could offer great potential to support the process of current citizen participation and to counteract the barriers that arise. Based on two focus groups with a total of 12 participants between the ages of 25 and 70, we investigated the barriers experienced and potential for optimisation of previous citizen participation as well as the expectations and preferences for digital citizen participation. The aim is to develop digital participation formats in a participatory manner based on the needs of citizens and thus utilize the potential of digital citizen participation. In the focus groups, we identified a lack of awareness of and identification with participation processes as possible personal barriers for citizens. A perceived lack of transparency and communication in participation processes were, among others, identified as barriers arising from project coordinators, which can lead to a lack of trust and frustration. In order to promote transparent and comprehensible participation processes in the future, the focus groups offer valuable information on the design of future citizen participation, both for process optimisation and for increasing awareness of citizen participation.

Future social robots will act autonomously in the world. Autonomous behavior is usually realized by using AI models built with real-world data, which often reflect existing inequalities and prejudices in society. Even if designers do not intend it, there are risks that robots will be developed that discriminate against certain users, e. g. based on gender. In this work, we investigate the implications of a gender-biased robot that disadvantages women, which unfortunately is a bias in AI that is often reported. Our experiment shows that both men and women perceive the gender-biased robot to be unfair. However, our work indicates that women are more aware that a gender bias causes this unfairness. We also show that gender bias results in the robot being perceived differently. While the gender bias resulted in lower likability and intelligence ratings by women, men seem to lose trust in the robot if it behaves unfairly.

We present Xperisight, an open-source tool for Extended Reality (XR) experiment supervision. Conducting experiments with XR devices already poses many technological and organizational challenges. However, scaling an experiment to larger numbers of participants is an even more complex endeavor. Thus, Xperisight provides remote access to Unity-based XR applications to oversee multiple sessions in parallel via one unified dashboard. Without influencing subjects by their presence, experimenters can access relevant information, remotely control the devices, and be called for help if questions or errors arise. The tool can be easily integrated with zero-code setup and minimal configuration and has already been successfully applied in a first experiment effectively halving the overall required experiment time.

In recent years, bicycle simulators have become increasingly important as a research tool, resulting in a large number of new prototypes at various academic institutions. In order for the results of studies to be transferred from a simulator to reality, it is necessary to ensure that simulators reproduce realistic traffic behaviour. Most bicycle simulators are evaluated using cycling parameters. However, the (combined) use of other methods is necessary to ensure realism. In this paper we present a variety of evaluation methods and discuss why we consider them relevant for evaluating the realism. We present the challenges and potential and the study design of our current study comparing cycling behaviour in reality and in simulation. Our study design should serve as a reference for other researchers and facilitate their own research. This paper contributes to the motivation of new and future work in the still young field of cycling simulation.