Human–Robot Interaction (HRI) education has largely emphasized technical skills, while psychological and socio-cognitive dimensions of interaction remain underrepresented. Yet contemporary HRI research increasingly views robots as social actors that shape perception, behavior, and decision-making. To address this gap, this paper introduces the Scientific HRI Didactic Framework (SHRI-DiF), a didactical approach that integrates research-based learning with the 5E cycle (Engage–Explore–Explain–Elaborate–Evaluate). The framework positions students as active researchers who conduct the full empirical process, from identifying research questions to evaluating findings. Social robots serve as epistemic tools and experimental agents to investigate social and psychological constructs such as trust, perceived control, and acceptance. The framework is demonstrated in a 12-week undergraduate seminar in business and media psychology using the Furhat robot for dialog strategies in HRI. Results indicate that authentic HRI research settings foster scientific reasoning, methodological competence, and interdisciplinary thinking, offering a transferable model for research-based higher education.

Abstract

As generative Artificial Intelligence (AI) becomes increasingly embedded and utilized for digital design, it presents both opportunities and risks. One major concern is its potential to facilitate and incorporate deceptive design patterns into computing technologies, which could manipulate or mislead users to their disadvantage. Similar to the concept of precedent-based design, a common approach in design theory that suggests reapplying previous design solutions to similar or identical problems, generative AI can integrate deceptive design patterns included in the training data a model has seen before. Our workshop explores how generative AI suggests and enacts deceptive design patterns in digital design. The goal of the workshop is to explore the ethical challenges of utilizing generative AI models and develop strategies to detect or prevent manipulative practices, thereby creating more transparent and equitable AI-generated experiences.

Deceptive patterns in visual interfaces - subtle nudges that persuade or manipulate users to take a certain action - have been researched extensively. Yet, little is known about how these patterns can be translated to the other human senses and what other forms of deception could be implemented for them. This meet-up aims to bring together researchers and practitioners with an interest in studying multi-sensory manipulative interface design, exploring how deceptive patterns for touch, sound, taste, and smell, but also lesser-known senses such as balance, pain, or interoception, can be created, embedded, and mitigated in and with current and future technologies. By facilitating the discussion of potential opportunities and risks across senses, we want to foster new collaborations to advance the field of responsible multisensory deceptive pattern research. In the long term, we hope to raise awareness of potential threats before they arise so we can better safeguard end users.

Alkoholkonsum zählt zu den bedeutendsten gesundheitsbezogenen Risikoverhaltensweisen, insbesondere unter jungen Erwachsenen. Die Wirksamkeit von Präventions- und Interventionsmaßnahmen hängt jedoch maßgeblich davon ab, die Zielgruppe und deren Motivation zu verstehen. Der vorliegende Beitrag identifiziert Mechanismen, die junge Erwachsene bei der Umsetzung einer bewussten Verhaltensänderung ihres Alkoholkonsums beeinflussen. Eine quantitative Studie unter 102 18-34-Jährigen zeigt, das die Problemrealisation und intrinsische Gründe zur Konsumänderung die kritische Selbstreflexion fördern, während extrinsische Gründe hemmend wirken. Negative Erlebnisse aufgrund von Alkoholkonsum wie Erinnerungslücken oder Kontrollverlust verstärken die Reflexion, während Schuldgefühle sie abschwächen. Die Befunde bestätigen, dass das Erkennen problematischer Trinkgewohnheiten eine kritische Selbstreflexion fördert, die wiederum mit einer aktiven Verhaltensänderung verbunden ist. Gleichsam zeigt sich, dass emotionale Reaktionen wie Schuldgefühle den Reflexionsprozess eher behindern als fördern können. Damit ist eine Differenzierung kognitiver und emotionaler Mechanismen nötig, um zu verstehen, wie Problembewusstsein für ungesunde Verhaltensweisen in aktive Verhaltensänderung überführt wird. Die Befunde liefern Implikationen für zielgruppenorientierte Präventionskampagnen und weisen auf die Bedeutung intrapersonaler Prozesse bei der Alkoholreduktion junger Erwachsener hin.

Immer mehr Menschen nutzen Sport-Tracking-Apps zur Überwachung ihres Trainings und zum sozialen Vergleich mit anderen. Sowohl leistungsbezogene Vergleichsprozesse als auch die User Experience der App können die Sportmotivation jedoch ambivalent beeinflussen: Während einige Nutzende durch Vernetzung und Feedback zusätzliche Motivation erfahren, erleben andere sozialen Druck und Demotivation. Diese widersprüchlichen Effekte sind für die nachhaltige Aufrechterhaltung sportlicher Aktivität von zentraler Bedeutung. Der vorliegende Beitrag untersucht den Einfluss von Selbstwirksamkeit und sozialem Vergleich sowie von UX-induziertem Kompetenz- und Beziehungserleben auf intrinsische und extrinsische Sportmotivation und deren Zusammenhang mit subjektivem Wohlbefinden. Eine quantitative Studie mit 101 Nutzenden von Sport-Tracking-Apps zeigt, dass soziale Vergleiche vor allem extrinsische Motivation fördern, während intrinsische Motivation unbeeinflusst bleibt. Selbstwirksamkeit hingegen stärkt intrinsische Motivation und reduziert extrinsische Regulation. Auch ein positives Kompetenzerleben im Nutzungskontext wirkt motivationsfördernd. Die Ergebnisse verdeutlichen die differenzierten Effekte psychologischer und UX-bezogener Faktoren und zeigen, dass intrapersonelle Ressourcen einen größeren Beitrag zur Sportmotivation leisten als gestalterische App-Merkmale. Damit wird deutlich, dass für digitale Gesundheitsanwendungen nicht allein das Interface-Design, sondern insbesondere individuelle Dispositionen entscheidend für sportbezogene Verhaltensintentionen sind.

This open access book offers a comprehensive exploration of digital health and care, addressing the profound transformations driven by digital technologies in the healthcare sector. It serves as a vital resource for understanding the evolution, impact and future directions of digital health innovations, providing readers with in-depth knowledge of regulatory frameworks, adoption strategies and patient-centric approaches. Drawing on perspectives from hospital care, ambulatory services, and long-term care, the book offers practical insights into implementation strategies, organizational change, and sector-specific challenges.

Digital health technologies are advancing rapidly and are beginning to reshape how care is accessed, delivered, and financed, creating new possibilities for more timely, patient-centered, and resilient health systems. At the same time, their large-scale adoption is constrained by persistent barriers such as unequal access and digital literacy, unresolved questions around data privacy, security and ownership, limited interoperability between fragmented systems, cultural resistance within organizations, and uncertain reimbursement and sustainability models. This chapter offers a concise map of this landscape by outlining key challenges, including the digital divide, governance of health data, integration and interoperability, change management, “pilotitis,” and ethical risks, as well as major opportunities, from AI-enabled decision support and personalized medicine to real-time analytics, value-based care, and gamified approaches to engagement. It also highlights how these issues intersect across regulatory frameworks, implementation strategies, equity considerations, and emerging innovation paradigms discussed in later chapters. It argues that the same technologies that can deepen fragmentation and inequity, if poorly governed, can also be harnessed through deliberate policy, design, and business models to generate durable, system-wide benefits.

German universities of Applied Sciences face declining student numbers and a growing shortage of skilled professionals in technical fields, prompting institutions in Bavaria to increasingly recruit international students, many of them from India. While this strategy stabilizes enrolment and supports national goals for securing a future workforce, it also introduces substantial challenges for teaching staff, administration, and students alike. Many incoming students bring competency profiles shaped by non-European higher-education systems, often marked by a strong emphasis on reproductive learning and limited experience with independent research, critical reflection, and scientific writing. High expectations of German master’s programs, combined with linguistic and cultural barriers, contribute to lower retention rates among international students and create additional burdens on teaching staff.

Drawing on experiences from four international master’s programs at Ansbach University of Applied Sciences, the paper analyses typical difficulties and evaluates measures designed to improve integration, academic success, and study conditions. Programs composed almost exclusively of Indian students tend to reproduce culturally homogeneous learning environments, which limit intercultural exchange, hinder discursive teaching formats, and reinforce established learning habits. In contrast, heterogeneously composed cohorts show better interaction, stronger language development, and improved academic performance. To counteract homogeneity, targeted interventions such as international poster sessions, mixed laboratory groups, and joint courses between German- and English-taught programs were introduced with positive but context-dependent outcomes.

Major challenges arise in the areas of scientific practice, rule compliance, and the unreflected use of AI tools, often rooted in a lack of prior exposure to principles of good scientific practice. The university responded with measures such as training units on academic integrity, adapted assessment formats emphasizing transfer performance, and workshops on literature research and academic writing. Additional structural factors such as particularly limited access to affordable local housing, negatively affect class attendance. Attempts to mitigate this included schedule adaptations to public transport and the introduction of block courses with mandatory practical components.

The paper concludes that successful internationalization requires more than English-language programs. It demands comprehensive support structures, including language training, intercultural competence development for staff, and institutional services tailored to international students’ needs. Given the intensive advising workload, smaller learning groups and recognition of international teaching efforts in workload models are essential. Only through sustained institutional commitment, adequate resources, and openness on all sides can internationalization efforts translate into improved learning outcomes and long-term integration.

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations.

This paper explores the association of anthropomorphism and cognitive load with respect to the influence of negative attitudes towards robots. The study consists in a cooperative pick-and-place task, where participants are required to repeatedly and alternatingly put a Lego brick onto one of two trays to be picked up and returned by a robot arm. The task is varied by whether or not participants had to remember an 8-digit number inducing extraneous cognitive load (within-subjects factor). Results show significant correlations of some dimensions of anthropomorphism and perceived cognitive load. However, dividing participants in groups with different negative attitudes towards robots, a significant difference of this association is found. This finding puts prior results on the dependency of anthropomorphism of robots and cognitive load into perspective and more research on the underlying cognitive processes is suggested.

This chapter addresses the ethical considerations essential for the responsible use of AI in healthcare, with a focus on marginalized populations such as ethnic minorities, people with disabilities, and underserved communities. It highlights key dimensions including data equity, algorithmic fairness, explainability, participatory design, and governance for accountability. The chapter emphasizes that AI systems must be trained on diverse and representative data to avoid perpetuating biases that disproportionately harm marginalized groups. Algorithmic fairness requires ongoing bias detection and mitigation, combined with transparent and interpretable AI models that build trust. Inclusive development through community participation ensures AI tools reflect the needs and values of those they serve. Strong governance frameworks, aligned with regulatory standards like the EU AI Act, are necessary to enforce accountability and protect vulnerable populations. When ethically implemented, AI can improve access, personalize care, and support proactive health management for underserved groups. However, the chapter cautions that these benefits depend on continuous commitment to equity, collaboration among stakeholders, and ongoing dialogue with affected communities to prevent exacerbating existing health disparities.

Digital communication has played an increasingly important role in schools around the world, especially since the COVID-19 pandemic. For professional communica tion and collaboration in particular, digital tools have provided teachers with the op portunity to collaborate location-independently and to easily exchange information and teaching materials. Hence, this study aimed to explore how teachers communi cate and collaborate digitally by examining differences in the use of instant messag ing and videoconferencing and the attitudes of teachers towards these technologies. Therefore, an online survey was conducted with primary and secondary school teachers from Germany (N = 250, 72.0% female). The analysis showed that mes sengers were used significantly more than videoconferences and that they differed in their usefulness for occasions of communication and collaboration. Structural equation modeling indicated that the self-assessed digital communication compe tence of teachers is both a significant predictor for the behavioral intention to use messengers as well as videoconferences, although the behavioral intention to use messengers in the future was significantly higher than for videoconferences. While a higher threshold to use videoconferences might be a reason for the differences that were identified in this study, further research into the communication and col laboration among teachers is still needed to understand the reasons for the differ ences in use.