CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent AgentsLarge language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems. However, they still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and an understanding of the ever-changing environment. To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across sessions for accurate responses. Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision making, that can contribute towards improved memory modeling for LLMs. Inspired by these principles, we propose CAIM, a cognitive AI memory framework that models key aspects of human memory through a multi-agent architecture. Specialized LLM-based agents handle memory-related functions such as retrieval, contextual relevance evaluation, and memory maintenance. We compare CAIM against existing approaches, focusing on metrics such as retrieval accuracy, response correctness, and contextual coherence. The results demonstrate that CAIM outperforms baseline frameworks in different metrics, highlighting its context awareness and demonstrating its contribution to memory mechanisms for improving long-term human-AI interactions.2026RWRebecca Westhäußer et al.Mercedes-Benz AGHuman-LLM CollaborationExplainable AI (XAI)AI-Assisted Decision-Making & AutomationIUI
Beyond Disposition: AI Knowledge Predicts Anthropomorphization of a Language Model Better Than Personality Traits in Lay and Expert PopulationsAnthropomorphizing Artificial Intelligence (AI), i.e., ascribing human-like mind or emotions to it, is widespread but varies across individuals. We tested three proposed dispositional predictors of anthropomorphism (need for cognition, need for structure, loneliness) in a general population (N = 307) and an AI expert sample (N = 130). Using a vignette design based on excerpts from a dialogue between the large language model LaMDA and one of its engineers, we found that none of the three dispositional traits predicted anthropomorphism. Instead, higher levels of AI knowledge decreased anthropomorphism across both samples. Experts reported higher AI knowledge and lower anthropomorphism than laypersons. For laypersons, anthropomorphism increased intentions to use LaMDA. For experts it did not, but was correlated with discomfort. In both samples, anthropomorphism was associated with greater moral care, i.e., not switching off LaMDA against "its will." Our findings highlight the role of knowledge and expertise in perceptions of AI.2026MMMartina Mara et al.Johannes Kepler University LinzAgent Personality & AnthropomorphismExplainable AI (XAI)AI Ethics, Fairness & AccountabilityCHI
Improving Low-Vision Chart Accessibility via On-Cursor Visual ContextDespite widespread use, charts remain largely inaccessible for Low-Vision Individuals (LVI). Reading charts requires viewing data points within a global context, which is difficult for LVI who may rely on magnification or experience a partial field of vision. We aim to improve exploration by providing visual access to critical context. To inform this, we conducted a formative study with five LVI. We identified four fundamental contextual elements common across chart types: axes, legend, grid lines, and the overview. We propose two pointer-based interaction methods to provide this context: Dynamic Context, a novel focus+context interaction, and Mini-map, which adapts overview+detail principles for LVI. In a study with N=22 LVI, we compared both methods and evaluated their integration to current tools. Our results show that Dynamic Context had significant positive impact on access, usability, and effort reduction; however, worsened visual load. Mini-map strengthened spatial understanding, but was less preferred for this task. We offer design insights to guide the development of future systems that support LVI with visual context while balancing visual load.2026YSYotam Sechayk et al.The University of TokyoVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Interactive Data VisualizationUncertainty VisualizationCHI
MIRAGE: Enabling Real-Time Automotive Mediated RealityTraffic is inherently dangerous, with around 1.19 million fatalities annually. Automotive Mediated Reality (AMR) can enhance driving safety by overlaying critical information (e.g., outlines, icons, text) on key objects to improve awareness, altering objects' appearance to simplify traffic situations, and diminishing their appearance to minimize distractions. However, real-world AMR evaluation remains limited due to technical challenges. To fill this sim-to-real gap, we present MIRAGE, an open-source tool that enables real-time AMR in real vehicles. MIRAGE implements 15 effects across the AMR spectrum of augmented, diminished, and modified reality using state-of-the-art computational models for object detection and segmentation, depth estimation, and inpainting. In an on-road expert user study (N=9) of MIRAGE, participants enjoyed the AMR experience while pointing out technical limitations and identifying use cases for AMR. We discuss these results in relation to prior work and outline implications for AMR ethics and interaction design.2026PJPascal Jansen et al.Ulm UniversityAutomated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)In-Vehicle Haptic, Audio & Multimodal FeedbackCHI
eHMI for All - Investigating the Effect of External Communication of Automated Vehicles on Pedestrians, Manual Drivers, and CyclistsWith automated vehicles (AVs), the absence of a human operator could necessitate external Human-Machine Interfaces (eHMIs) to communicate with other road users. Existing research primarily focuses on pedestrian-AV interactions, with limited attention given to other road users, such as cyclists and drivers of manually driven vehicles. So far, no studies have compared the effects of eHMIs across these three road user roles. Therefore, we conducted a within-subjects virtual reality experiment (N=40), evaluating the subjective and objective impact of an eHMI communicating the AV's intention to pedestrians, cyclists, and drivers under various levels of distraction (no distraction, visual noise, interference). eHMIs positively influenced safety perceptions, trust, perceived usefulness, and mental demand across all roles. While distraction and road user roles showed significant main effects, interaction effects were only observed in perceived usability. Thus, a unified eHMI design is effective, facilitating the standardization and broader adoption of eHMIs in diverse traffic.2026MCMark Colley et al.Ulm UniversityExternal HMI (eHMI) — Communication with Pedestrians & CyclistsCHI
Investigating the Effects of Eco-Friendly Service Options on Rebound Behavior in Ride-HailingEco-friendly service options (EFSOs) aim to reduce personal carbon emissions, yet their eco-friendly framing may permit increased consumption, weakening their intended impact. Such rebound effects remain underexamined in HCI, including how common eco-feedback approaches shape them. We investigate this in an online within-subjects experiment (N=75) in a ride-hailing context. Participants completed 10 trials for five conditions (No EFSO, EFSO - Minimal, EFSO - CO2 Equivalency, EFSO - Gamified, EFSO - Social), yielding 50 choices between walking and ride-hailing for trips ranging from 0.5mi - 2.0mi (≈ 0.80km - 3.22km). We measured how different EFSO variants affected ride-hailing uptake relative to a No EFSO baseline. EFSOs lacking explicit eco-feedback metrics increased ride-hailing uptake, and qualitative responses indicate that EFSOs can make convenience-driven choices more permissible. We conclude with implications for designing EFSOs that begin to take rebound effects into account.2026AZAlbin Zeqiri et al.Ulm UniversityRidesharing PlatformsSustainable HCIEnergy Conservation Behavior & InterfacesCHI
ProVoice: Designing Proactive Functionality for In-Vehicle Conversational Assistants using Multi-Objective Bayesian Optimization to Enhance Driver ExperienceThe next step for In-vehicle Conversational Assistants (IVCAs) will be their capability to initiate and automate proactive system interactions throughout journeys. However, diverse drivers make it challenging to design voice interventions tailored towards individual on-road expectations. This paper evaluates the effectiveness of Human-in-the-Loop (HITL) Multi-Objective Bayesian Optimization (MOBO) in design by implementing ProVoice: a Virtual Reality (VR) driving simulator integrating MOBO to investigate the effects of IVCA design variants on perceived mental demand, predictability, and usefulness. By reporting the Pareto Front from a within-subjects VR study (N=19), this paper proposes optimal design trade-offs. Follow-up analysis demonstrates MOBO’s success in discovering effective intervention strategies, with reduced participant mental demand, alongside enhanced predictability and usefulness while engaging with the proactive IVCA. Implications for computational techniques in future research on proactive intervention strategies are discussed. ProVoice can extend to include alternative design parameters and driving scenarios, encouraging intervention design on a broad scale.2026JSJosh Susak et al.UCL Interaction CentreAutomated Driving Interface & Takeover DesignVoice User Interface (VUI) DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)CHI
Responsible Trauma Research: Designing Effective and Sustainable Virtual Reality Exposure StudiesVirtual reality exposure therapy (VRET) enables controlled exposure to trauma-related stimuli to facilitate memory access and emotional processing. However, the field remains underexplored for complex post-traumatic stress disorder (C-PTSD). Unlike single-trauma PTSD, C-PTSD requires highly individualized triggers that are difficult to identify and implement safely. We conducted a feasibility study with 11 patients, two trauma therapists, and a VR developer to explore integrating VRET into C-PTSD treatment while safeguarding all stakeholders. Initial findings indicate that simple objects can be just as effective as complex scenes, therapeutic success does not correlate with VR presence levels, and the design process itself became integral to therapy rather than preparatory. However, involving developers in therapy sessions led to considerable emotional stress and role confusion, which required a cautious approach. Based on these insights, we provide methodological recommendations for safe and patient-centered VRET studies that balance therapeutic effectiveness with stakeholder safety across the research process.2026ADAnnalisa Degenhard et al.University of UlmVR Medical Training & RehabilitationMental Health Apps & Online Support CommunitiesAffective Feedback & Emotion Regulation InterfacesCHI
VIP-Sim: A User-Centered Approach to Vision Impairment Simulation for Accessible DesignPeople with vision impairments (VIPs) often rely on their remaining vision when interacting with user interfaces. Simulating visual impairments is an effective tool for designers, fostering awareness of the challenges faced by VIPs. While previous research has introduced various vision impairment simulators, none have yet been developed with the direct involvement of VIPs or thoroughly evaluated from their perspective. To address this gap, we developed VIP-Sim. This symptom-based vision simulator was created through a participatory design process tailored explicitly for this purpose, involving N=7 VIPs. 21 symptoms, like field loss or light sensitivity, can be overlaid on desktop design tools. Most participants felt VIP-Sim could replicate their symptoms. VIP-Sim was received positively, but concerns about exclusion in design and comprehensiveness of the simulation remain, mainly whether it represents the experiences of other VIPs.2025MRMax Rädler et al.Visual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Universal & Inclusive DesignParticipatory DesignUIST
Long-Term Evolution of Driver Visual Attention during Automated Driving in Real-Traffic: Investigating the Influence of Mental Model and Dynamic Learned TrustA calibrated trust level is essential for the safe use of automated systems. In automated driving, overtrust can reduce the driver’s monitoring behavior and delay takeover times, which poses significant safety risks. This motivates the need for continuous, objective trust assessment for real-time system adaptations. Prior research identified eye-tracking as a promising approach. Therefore, this study examines the longitudinal relationship between dynamic learned trust and visual attention. Given that mental models influence both trust and visual attention, their role in this process is also explored over time. In a longitudinal study, twenty-three participants repeatedly operated an automated vehicle in real traffic while their visual attention was recorded via the vehicle’s built-in driver monitoring camera. Findings suggest an interrelation between dynamic learned trust and mental model formation, with mental models mediating the effect of dynamic learned trust on visual attention. This work contributes to advancing trust measurement during automated driving.2025SSStephanie Seupke et al.Automated Driving Interface & Takeover DesignEye Tracking & Gaze InteractionAutoUI
Unraveling Subjective ADAS Comprehension Considering Factors of Situational Complexity on the Example of Traffic Light ScenariosAdvanced driver assistance systems (ADAS) with increasing automation maturity and availability in urban contexts are entering the market. Meanwhile, the situational context has been identified to play a crucial role in system comprehension and usage, yet its subcomponents and their relation to system comprehension remain an open research question. To gain insights in the role of the situation complexity regarding subjective system comprehension and different methodological aspects, this study applies a mixed quantitative and qualitative approach, focusing on signaled intersections as an exemplary scenario. An on-road study with forty-six participants was conducted, involving six traffic light scenarios (all experienced twice). Results indicate that while comprehension was generally high, the situational context, including environmental and traffic-related factors, affected subjective system understanding. The proposed approach sheds light on the role of mixed methods in ADAS research, which may provide insights for system developers and suggestions for user training content.2025CBClaudia Buchner et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AutoUI
Mind Games! Exploring the Impact of Dark Patterns in Mixed Reality ScenariosMixed Reality (MR) integrates virtual objects with the real world, offering potential but raising concerns about misuse through dark patterns. This study explored the effects of four dark patterns, adapted from prior research, and applied to MR across three targets: places, products, and people. In a two-factorial within-subject study with 74 participants, we analyzed 13 videos simulating MR experiences during a city walk. Results show that all dark patterns significantly reduced user comfort, increased reactance, and decreased the intention to use MR glasses, with the most disruptive effects linked to personal or monetary manipulation. Additionally, the dark patterns of Emotional and Sensory Manipulation and Hiding Information produced similar impacts on the user in MR, suggesting a re-evaluation of current classifications to go beyond deceptive design techniques. Our findings highlight the importance of developing ethical design guidelines and tools to detect and prevent dark patterns as immersive technologies continue to evolve.2025LMLuca-Maxim Meinhardt et al.Mixed Reality WorkspacesDark Patterns RecognitionMobileHCI
Introducing ROADS: A Systematic Comparison of Remote Control Interaction Concepts for Automated Vehicles at Road WorksAs vehicle automation technology continues to mature, there is a necessity for robust remote monitoring and intervention features. These are essential for intervening during vehicle malfunctions, challenging road conditions, or in areas that are difficult to navigate. This evolution in the role of the human operator—from a constant driver to an intermittent teleoperator—necessitates the development of suitable interaction interfaces. While some interfaces were suggested, a comparative study is missing. We designed, implemented, and evaluated three interaction concepts (path planning, trajectory guidance, and waypoint guidance) with up to four concurrent requests of automated vehicles in a within-subjects study with N=23 participants. The results showed a clear preference for the path planning concept. It also led to the highest usability but lower satisfaction. With trajectory guidance, the fewest requests were resolved. The study’s findings contribute to the ongoing development of HMIs focused on the remote assistance of automated vehicles.2025MCMark Colley et al.Ulm University; UCL Interaction CentreAutomated Driving Interface & Takeover DesignTeleoperated DrivingCHI
Light My Way. Developing and Exploring a Multimodal Interface to Assist People With Visual Impairments to Exit Highly Automated VehiclesThe introduction of Highly Automated Vehicles (HAVs) has the potential to increase the independence of blind and visually impaired people (BVIPs). However, ensuring safety and situation awareness when exiting these vehicles in unfamiliar environments remains challenging. To address this, we conducted an interactive workshop with N=5 BVIPs to identify their information needs when exiting an HAV and evaluated three prior-developed low-fidelity prototypes. The insights from this workshop guided the development of PathFinder, a multimodal interface combining visual, auditory, and tactile modalities tailored to BVIP's unique needs. In a three-factorial within-between-subject study with N=16 BVIPs, we evaluated PathFinder against an auditory-only baseline in urban and rural scenarios. PathFinder significantly reduced mental demand and maintained high perceived safety in both scenarios, while the auditory baseline led to lower perceived safety in the urban scenario compared to the rural one. Qualitative feedback further supported PathFinder's effectiveness in providing spatial orientation during exiting.2025LMLuca-Maxim Meinhardt et al.Institute of Media Informatics, Ulm UniversityIn-Vehicle Haptic, Audio & Multimodal FeedbackVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)CHI
Improving External Communication of Automated Vehicles Using Bayesian OptimizationThe absence of a human operator in automated vehicles (AVs) may require external Human-Machine Interfaces (eHMIs) to facilitate communication with other road users in uncertain scenarios, for example, regarding the right of way. Given the plethora of adjustable parameters, balancing visual and auditory elements is crucial for effective communication with other road users. With N=37 participants, this study employed multi-objective Bayesian optimization to enhance eHMI designs and improve trust, safety perception, and mental demand. By reporting the Pareto front, we identify optimal design trade-offs. This research contributes to the ongoing standardization efforts of eHMIs, supporting broader adoption.2025MCMark Colley et al.Ulm University; UCL Interaction CentreExternal HMI (eHMI) — Communication with Pedestrians & CyclistsExplainable AI (XAI)CHI
PlantPal: Leveraging Precision Agriculture Robots to Facilitate Remote Engagement in Urban GardeningUrban gardening is widely recognized for its numerous health and environmental benefits. However, the lack of suitable garden spaces, demanding daily schedules and limited gardening expertise present major roadblocks for citizens looking to engage in urban gardening. While prior research has explored smart home solutions to support urban gardeners, these approaches currently do not fully address these practical barriers. In this paper, we present PlantPal, a system that enables the cultivation of garden spaces irrespective of one's location, expertise level, or time constraints. PlantPal enables the shared operation of a precision agriculture robot (PAR) that is equipped with garden tools and a multi-camera system. Insights from a 3-week deployment (N=18) indicate that PlantPal facilitated the integration of gardening tasks into daily routines, fostered a sense of connection with one's field, and provided an engaging experience despite the remote setting. We contribute design considerations for future robot-assisted urban gardening concepts.2025AZAlbin Zeqiri et al.Ulm University, Institute of Media InformaticsHuman-Robot Collaboration (HRC)Community Engagement & Civic TechnologyCHI
OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User ExperienceAutomated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.2025PJPascal Jansen et al.Ulm University, Institute of Media InformaticsHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AI-Assisted Decision-Making & AutomationCHI
When Do We Feel Present in a Virtual Reality? Towards Sensitivity and User Acceptance of Presence QuestionnairesPresence is an important and widely used metric to measure the quality of virtual reality (VR) applications. Given the multifaceted and subjective nature of presence, the most common measures for presence are questionnaires. But there is little research on their validity regarding specific presence dimensions and their responsiveness to differences in perception among users. We investigated four presence questionnaires (SUS, PQ, IPQ, Bouchard) on their responsiveness to intensity variations of known presence dimensions and asked users about their consistency with their experience. Therefore, we created five VR scenarios that were designed to emphasize a specific presence dimension. Our findings showed heterogeneous sensitivity of the questionnaires dependent on the different dimensions of presence. This highlights a context-specific suitability of presence questionnaires. The questionnaires' sensitivity was further stated as lower than actually perceived. Based on our findings, we offer guidance on selecting these questionnaires based on their suitability for particular use cases.2025ADAnnalisa Degenhard et al.University of Ulm, Media informaticsImmersion & Presence ResearchCHI
Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social MediaInfinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users' reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention's effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.2025LMLuca-Maxim Meinhardt et al.Institute of Media Informatics, Ulm UniversityNotification & Interruption ManagementCHI
Bumpy Ride? Understanding the Effects of External Forces on Spatial Interactions in Moving VehiclesAs the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, interaction methods are susceptible to physical motion, leading to input errors and reduced task performance. This work investigates the impact of G-forces, vibrations, and unpredictable maneuvers on 3D interaction methods. We conducted a field study with 24 participants in both stationary and moving vehicles to examine the effects of vehicle motion on four interaction methods: (1) Gaze\&Pinch, (2) DirectTouch, (3) Handray, and (4) HeadGaze. Participants performed selections in a Fitts' Law task. Our findings reveal a significant effect of vehicle motion on interaction accuracy and duration across the tested combinations of Interaction Method $\times$ Road Type $\times$ Curve Type. We found a significant impact of movement on throughput, error rate, and perceived workload. Finally, we propose future research considerations and recommendations on interaction methods during vehicle movement.2025MSMarkus Sasalovici et al.Mercedes-Benz Tech Motion GmbH; Ulm University, Institute of Media InformaticsHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Motion Sickness & Passenger ExperienceCHI