Veranstaltungsprogramm

Automation technology has profoundly transformed modern life, promising further evolution in safety and efficiency. However, it also fundamentally alters work dynamics, notably in the maritime sector where automation is increasingly prevalent. This dissertation investigates how automated systems impact mental workload and human-technology interactions in maritime contexts. By adapting a framework based on cognitive load theory, it analyzes predictors (e.g., automation, system design, level of autonomy, individual differences) of mental workload through systematic reviews and experimental studies. Key challenges include recruiting specialized maritime participants and deploying equipment in operational settings. By addressing these challenges, the dissertation aims to enhance understanding and implementation of automation, offering practical insights for optimizing human-technology interfaces in maritime automation.

The PhD thesis explores the integration of patient generated health data into healthcare systems. At a broader level, I explore challenges to integration of patient generated health data from the perspectives of Healthcare professionals and patients while engaging in shared decision making. This resulted in a 10 stage workflow model for integrating patient generated health data in healthcare settings. Next I focus on how patient generated health data can be integrated into clinical pathways involving physical activity planning of cardiovascular disease patients during cardiac rehabilitation. Preliminary findings emphasise the need to focus on HCP data and information needs to effectively integrate patient generated health data in clinical workflows. As a followup, I explore the use of generativeAI to support sensemaking of patient generated health data amongst patients and HCPs. Finally, I focus on the privacy preferences of patient generated health data by designing for dynamic consent while they share their data with HCPs or researchers.

Generative artificial intelligence (GenAI), particularly large language models (LLMs), offer new capabilities of natural language understanding and generation, potentially reducing employee stress and high turnover rates in customer service delivery. However, these systems also present risks, such as generating convincing but erroneous responses, known as hallucinations and confabulations. Thus, this study investigates the impact of GenAI on service performance in customer support settings, emphasizing augmentation over automation to address three key inquiries: identifying patterns of GenAI infusion that alter service routines, assessing the effects of human-AI interaction on cognitive load and task performance, and evaluating the role of explainable AI (XAI) in detecting erroneous responses such as hallucinations. Employing a design science research approach, the study combines literature reviews, expert interviews, and experimental designs to derive implications for designing GenAI-driven augmentation. Preliminary findings reveal three key insights: (1) Service employees play a critical role in retaining organizational knowledge and delegating decisions to GenAI agents; (2) Utilizing GenAI co-pilots significantly reduces the cognitive load during stressful customer interactions; and (3) Novice employees face challenges in discerning accurate AI-generated advice from inaccurate suggestions without additional explanatory context.

This research project investigates how adaptive Augmented Reality (AR) applications can improve the efficiency of drone operations and the experience of operators. Drones, particularly Unmanned Aerial Vehicles (UAVs), are increasingly employed in search and rescue (SAR) missions due to their rapid deployment capabilities and the ability to access hazardous areas or viewpoints that are otherwise unreachable. Operating drones in these high-demanding scenarios requires operators to possess certain cognitive abilities to manage the UAV effectively. Our research aims to develop a situation-aware interaction model that considers data from the task, the UAVs, and the human operator, offering support during demanding missions or low abilities. This model will be integrated into an AR application designed to adjust to the current situation, thereby enhancing operational outcomes and user experience.

This research investigates the scalability and individualisation challenges of online practical assignments in online learning modules. Utilising a MOOC prototype in a blended learning framework, the study employs the Action Design Research (ADR) method to enhance online learning. Key interventions include chatbots for task provision and feedback, aiming to offer personalised and scalable solutions. The study will evaluate these interventions' impact on learning outcomes and develop a comprehensive conceptual framework. Findings will provide insights into effective technological solutions, contributing significantly to the field of Human-Computer Interaction (HCI) and online education.

This doctoral consortium proposal outlines the research project titled "Extended Perception Layer (XPL) – Investigating a Holistic Digital Environment." The study addresses the emerging need to integrate Augmented Reality (AR) within the broader context of the Metaverse. As digital and physical worlds increasingly intersect, this research aims to conceptualize a comprehensive AR framework that enhances user interaction across private, shared, and public layers. Grounded in the level of analysis theory, the project seeks to explore the societal and individual impacts of a pervasive digital layer overlaying the physical environment. Preliminary work includes conceptualizing XPL and investigating user concerns related to everyday AR use. Key research questions focus on identifying components of a holistic digital layer, understanding user behavior in various contexts, and designing effective virtual objects. The project involves mixed-method studies to explore immersive environments' effects on well-being, social presence, and personal space in collaborative settings. Additionally, design principles will be developed to mitigate visual clutter and ensure safe AR interactions. This research contributes to the theoretical foundation and practical implementation of AR in enhancing human perception and interaction within the Metaverse, aiming for a seamless digital-physical integration.

Many approaches tried to make the traditional input method of keyboard and mouse more ergonomically pleasant to use or to exchange the used hardware. One of those approaches is the use of eye tracking, which tries to solve many of the efficiency and health problems by using gaze as input. One key challenge with those systems is the imprecision of eye tracking hardware, resulting from hardware as well as physiological limitations. The main goal of this research work is to create a system, that improves the accuracy of gaze-based input devices on an application level.

Artificial intelligence (AI) has become an omnipresent part of everyday life and increasingly also of the world of work. The design of human-AI interaction plays a crucial role in this context. This project aims to analyze the effect of anthropomorphic design elements on perceived trustworthiness. Positive and negative consequences are to be investigated in a mixed-methods approach in realistic laboratory settings. The aim is to develop guidelines for the design of anthropomorphic AI systems in a work context.