arXiv subjects

Albrecht Schmidt

Publications and source records attributed to Albrecht Schmidt.

At least 19 recordsLinked to original sources

arXiv2026

Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction

Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such data could transform preventative care, its integration into clinical practice is hindered by its scale, heterogeneity and the time pressure and data literacy of healthcare professionals (HCPs). We explore how large language models (LLMs) can support sensemaking of patient-generated health data (PGHD) with automated summaries and natural language data exploration. Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed multimodal PGHD in a mixed-methods study with a prototype that integrated common charts, LLM-generated summaries, and a conversational interface. Findings show that AI summaries provided quick overviews that anchored exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance. We contribute empirical insights and sociotechnical design implications for integrating AI-driven summarization and conversation into clinical workflows to support PGHD sensemaking.

cs.HC↗

arXiv2025

Towards Data-Enabled Physical Activity Planning: An Exploratory Study of HCP Perspectives On The Integration Of Patient-Generated Health Data

Physical activity planning is an essential part of cardiovascular rehabilitation. Through a two-part formative design exploration, we investigated integrating patient-generated health data (PGHD) into clinical workflows supporting shared decision-making (SDM) in physical activity planning. In part one, during a two-week situated study, to reduce risk of working with cardiovascular disease patients, we recruited healthy participants who self-tracked health and physical activity data and attended a physical activity planning session with a healthcare professional (HCP). Subsequently both HCPs and participants were interviewed. In part two, findings from part one were presented to HCPs in a card-sorting workshop to corroborate findings and identify information needs of HCPs alongside patient journeys and clinical workflows. Our outcomes highlight HCP information needs around patient risk factors, vital signs, and adherence to physical activity. Enablers for PGHD integration include adaptive data sense-making, standardization and organizational support for integration. Barriers include lack of time, data quality, trust and liability concerns. Our research highlights implications for designing digital health technologies that support PGHD in physical activity planning during cardiac rehabilitation.

cs.HC↗

arXiv2025

Designing Intent Communication for Agent-Human Collaboration

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents' intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user preferences. A key gap remains: existing models of what to communicate are rarely linked to systematic choices of how and when to communicate, preventing the development of generalizable, multi-modal strategies. In this paper, we introduce a multidimensional design space for intent communication structured along three dimensions: Transparency (what is communicated), Abstraction (when), and Modality (how). We apply this design space to three distinct human-agent collaboration scenarios: (a) bystander interaction, (b) cooperative tasks, and (c) shared control, demonstrating its capacity to generate adaptable, scalable, and cross-domain communication strategies. By bridging the gap between intent content and communication implementation, our design space provides a foundation for designing safer, more intuitive, and more transferable agent-human interactions.

cs.HC↗

arXiv2025

Through the Expert's Eyes: Exploring Asynchronous Expert Perspectives and Gaze Visualizations in XR

Transferring knowledge across generations is fundamental to human civilization, yet the challenge of passing on complex practical skills persists. Methods without a physically present instructor, such as videos, often fail to explain complex manual tasks, where spatial and social factors are critical. Technologies such as eXtended Reality and Artificial Intelligence hold the potential to retain expert knowledge and facilitate the creation of tailored, contextualized, and asynchronous explanations regardless of time and place. In contrast to videos, the learner's perspective can be different from the recorded perspective in XR. This paper investigates the impact of asynchronous first- and third-person perspectives and gaze visualizations on efficiency, feeling of embodiment, and connectedness during manual tasks. The empirical results of our study (N=36) show that the first-person perspective is better in quantitative measures and preferred by users. We identify best practices for presenting preserved knowledge and provide guidelines for designing future systems.

cs.HC↗

arXiv2025

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces

As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we propose a multidimensional design space that maps intent content (SAT1, SAT3), planning horizon (operational to strategic), and modality (visual, auditory, haptic). We illustrate how this space can guide the design of multimodal communication strategies tailored to dynamic collaborative work contexts. With this paper, we lay the conceptual foundation for a future design toolkit aimed at supporting transparent human-robot interaction in the workplace. We highlight key open questions and design challenges, and propose a shared agenda for multimodal, adaptive, and trustworthy robotic collaboration in hybrid work environments.

cs.HC↗

arXiv2024

An Approach to Elicit Human-Understandable Robot Expressions to Support Human-Robot Interaction

Understanding the intentions of robots is essential for natural and seamless human-robot collaboration. Ensuring that robots have means for non-verbal communication is a basis for intuitive and implicit interaction. For this, we contribute an approach to elicit and design human-understandable robot expressions. We outline the approach in the context of non-humanoid robots. We paired human mimicking and enactment with research from gesture elicitation in two phases: first, to elicit expressions, and second, to ensure they are understandable. We present an example application through two studies (N=16 \& N=260) of our approach to elicit expressions for a simple 6-DoF robotic arm. We show that it enabled us to design robot expressions that signal curiosity and interest in getting attention. Our main contribution is an approach to generate and validate understandable expressions for robots, enabling more natural human-robot interaction.

cs.RO↗

arXiv2024

Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their performance by four points. Interestingly, higher AI literacy was linked to less accurate self-assessment. Participants with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning-Kruger effect, usually observed in this task, ceased to exist with AI. Study 2 (N = 452) replicates these findings. We discuss how AI levels metacognitive performance and consider consequences of performance overestimation for interactive AI systems enhancing cognition.

cs.HC↗

arXiv2024

"If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishment

Large language models (LLMs) like ChatGPT have been widely adopted in work contexts. We explore the impact of ChatGPT on young professionals' perception of productivity and sense of accomplishment. We collected LLMs' main use cases in knowledge work through a preliminary study, which served as the basis for a two-week diary study with 21 young professionals reflecting on their ChatGPT use. Findings indicate that ChatGPT enhanced some participants' perceptions of productivity and accomplishment by enabling greater creative output and satisfaction from efficient tool utilization. Others experienced decreased perceived productivity and accomplishment, driven by a diminished sense of ownership, perceived lack of challenge, and mediocre results. We found that the suitability of task delegation to ChatGPT varies strongly depending on the task nature. It's especially suitable for comprehending broad subject domains, generating creative solutions, and uncovering new information. It's less suitable for research tasks due to hallucinations, which necessitate extensive validation.

cs.HC↗

arXiv2024

The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual's current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.

cs.HC↗

arXiv2024

HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation

Routes represent an integral part of triggering emotions in drivers. Navigation systems allow users to choose a navigation strategy, such as the fastest or shortest route. However, they do not consider the driver's emotional well-being. We present HappyRouting, a novel navigation-based empathic car interface guiding drivers through real-world traffic while evoking positive emotions. We propose design considerations, derive a technical architecture, and implement a routing optimization framework. Our contribution is a machine learning-based generated emotion map layer, predicting emotions along routes based on static and dynamic contextual data. We evaluated HappyRouting in a real-world driving study (N=13), finding that happy routes increase subjectively perceived valence by 11% (p=.007). Although happy routes take 1.25 times longer on average, participants perceived the happy route as shorter, presenting an emotion-enhanced alternative to today's fastest routing mechanisms. We discuss how emotion-based routing can be integrated into navigation apps, promoting emotional well-being for mobility use.

cs.HC↗

arXiv2023

Gender, Age, and Technology Education Influence the Adoption and Appropriation of LLMs

Large Language Models (LLMs) such as ChatGPT have become increasingly integrated into critical activities of daily life, raising concerns about equitable access and utilization across diverse demographics. This study investigates the usage of LLMs among 1,500 representative US citizens. Remarkably, 42% of participants reported utilizing an LLM. Our findings reveal a gender gap in LLM technology adoption (more male users than female users) with complex interaction patterns regarding age. Technology-related education eliminates the gender gap in our sample. Moreover, expert users are more likely than novices to list professional tasks as typical application scenarios, suggesting discrepancies in effective usage at the workplace. These results underscore the importance of providing education in artificial intelligence in our technology-driven society to promote equitable access to and benefits from LLMs. We urge for both international replication beyond the US and longitudinal observation of adoption.

cs.CY↗

arXiv2023

Useful but Distracting: Keyword Highlights and Time-Synchronization in Captions for Language Learning

Captions provide language learners with a scaffold for comprehension and vocabulary acquisition. Past work has proposed several enhancements such as keyword highlights for increased learning gains. However, little is known about learners' experience with enhanced captions, although this is critical for adoption in everyday life. We conducted a survey and focus group to elicit learner preferences and requirements and implemented a processing pipeline for enhanced captions with keyword highlights, time-synchronized keyword highlights, and keyword captions. A subsequent online study (n = 49) showed that time-synchronized keyword highlights were the preferred design for learning but were perceived as too distracting to replace standard captions in everyday viewing scenarios. We conclude that keyword highlights and time-synchronization are suitable for integrating learning into an entertaining everyday-life activity, but the design should be optimized to provide a more seamless experience.

cs.HC↗

arXiv2023

In Sync: Exploring Synchronization to Increase Trust Between Humans and Non-humanoid Robots

When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research found that this synchronization is a crucial aspect of human relations that strengthens social cohesion and trust. Generalizing from these findings in synchronization theory, we propose a dynamical approach that can be applied in the design of non-humanoid robots to increase trust. We contribute the results of a controlled experiment with 51 participants exploring our concept in a between-subjects design. For this, we built a prototype of a simple non-humanoid robot that can bend to follow human movements and vary the movement synchronization patterns. We found that synchronized movements lead to significantly higher ratings in an established questionnaire on trust between people and automation but did not influence the willingness to spend money in a trust game.

cs.HC↗

arXiv2023

Understanding the Uncertainty Loop of Human-Robot Interaction

Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the humans' current state and give an unexpected response. In human-human conversations, unexpected responses happen frequently. However, it is currently unclear how such robots should act if they understand that the human did not expect their response, or even showing the uncertainty of their response in the first place. For this, we explore the different forms of potential uncertainties during human-robot conversations and how humanoids can, through verbal and non-verbal cues, communicate these uncertainties.

cs.HC↗

arXiv2023

The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect, and higher levels of participants' influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.

cs.HC↗

arXiv2023

The Walking Talking Stick: Understanding Automated Note-Taking in Walking Meetings

While walking meetings offer a healthy alternative to sit-down meetings, they also pose practical challenges. Taking notes is difficult while walking, which limits the potential of walking meetings. To address this, we designed the Walking Talking Stick -- a tangible device with integrated voice recording, transcription, and a physical highlighting button to facilitate note-taking during walking meetings. We investigated our system in a three-condition between-subjects user study with thirty pairs of participants ($N$=60) who conducted 15-minute outdoor walking meetings. Participants either used clip-on microphones, the prototype without the button, or the prototype with the highlighting button. We found that the tangible device increased task focus, and the physical highlighting button facilitated turn-taking and resulted in more useful notes. Our work demonstrates how interactive artifacts can incentivize users to hold meetings in motion and enhance conversation dynamics. We contribute insights for future systems which support conducting work tasks in mobile environments.

cs.HC↗

arXiv2023

Feeling the Temperature of the Room: Unobtrusive Thermal Display of Engagement during Group Communication

Thermal signals have been explored in HCI for emotion-elicitation and enhancing two-person communication, showing that temperature invokes social and emotional signals in individuals. Yet, extending these findings to group communication is missing. We investigated how thermal signals can be used to communicate group affective states in a hybrid meeting scenario to help people feel connected over a distance. We conducted a lab study (N=20 participants) and explored wrist-worn thermal feedback to communicate audience emotions. Our results show that thermal feedback is an effective method of conveying audience engagement without increasing workload and can help a presenter feel more in tune with the audience. We outline design implications for real-world wearable social thermal feedback systems for both virtual and in-person communication that support group affect communication and social connectedness. Thermal feedback has the potential to connect people across distances and facilitate more effective and dynamic communication in multiple contexts.

cs.HC↗

arXiv2023

Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory

Social media platforms use short, highly engaging videos to catch users' attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions. We conducted a between-subjects experiment (N=60) investigating the impact of engaging with TikTok, Twitter, and YouTube while performing a Prospective Memory task (i.e., executing a previously planned action). The study required participants to remember intentions over interruptions. We found that the TikTok condition significantly degraded the users' performance in this task. As none of the other conditions (Twitter, YouTube, no activity) had a similar effect, our results indicate that the combination of short videos and rapid context-switching impairs intention recall and execution. We contribute a quantified understanding of the effect of social media feed format on Prospective Memory and outline consequences for media technology designers to not harm the users' memory and wellbeing.

cs.HC↗