Attention Deficit Hyperactivity Disorder (ADHD), characterized by inattention, hyperactivity, and impulsivity, is prevalent in the adult population. Long perceived and treated as a childhood condition, ADHD and its characteristics nonetheless impact a significant portion of adults today. In contrast to children with ADHD, adults with ADHD face unique challenges in the workplace and in higher education. In this work-in-progress paper, we present a scoping review as a foundation to understand and explore existing technology-based approaches to support adults with ADHD. In total, our search returned 3,538 papers upon which we selected, based on PRISMA-ScR, a total of 46 papers for in-depth analysis. Our initial findings highlight that most papers take on a therapeutic or intervention perspective instead of a more positive support perspective. Our analysis also found a tremendous increase in recent papers on the topic, which highlights that more and more researchers are becoming aware of the need to address ADHD with adults. For the future, we aim to further analyze the corpus and identify research gaps and potentials for further development of ADHD assistive technologies.

Hybrid collaboration has become a fixture in modern workplaces, yet it introduces persistent socio-technical asymmetries-especially disadvantaging remote participants, who struggle with presence disparity, reduced visibility, and limited non-verbal communication. Traditional solutions often seek to erase these asymmetries, but recent research suggests embracing them as productive design constraints. In this context, we introduce NoticeLight: a tangible, peripheral robotic embodiment designed to augment hybrid meetings. NoticeLight transforms remote participants' digital presence into ambient, physical signals – such as mood dynamics, verbal contribution mosaics, and attention cues – within the co-located space. By abstracting group states into subtle light patterns, NoticeLight fosters peripheral awareness and balanced participation without disrupting meeting flow or demanding cognitive overload. This approach aligns with emerging perspectives in human-robot synergy, positioning robots as mediators that reshape, rather than replicate, human presence. Our work thereby advances the discourse on how robotic embodiments can empower equitable, dynamic collaboration in the workplace.

We present FallacyCheck, a proactive Large Language Model (LLM)-based browser extension designed to motivate the critical assessment of news articles by questioning logical fallacies. Existing LLM-based extensions are reactive, limiting their ability to inoculate users against information disorder. FallacyCheck overcomes this by proactively identifying logical fallacies, such as "Appeal to Emotion" and "Ad Hominem", and most importantly, motivating users to critically assess the article by posing them a thought-provoking, non-leading question in a contextual tooltip. This design, rooted in inoculation theory, prompts users toward meta-cognitive reflection on the argument's logical structure. A preliminary evaluation with 16 highschool students showed the tool was perceived to be easy to use, and the questions helped to stimulate critical thinking. Crucially, participants generally would not have posed these critical questions without the tool's support.

Social media platforms use multimodal data (e.g., text, images, behavioral patterns) to infer user characteristics for algorithmic profiling. To comply with privacy regulations like the GDPR, companies provide transparency tools, which are often hard for users to interpret—especially individuals with cognitive impairments (CIs), whose specific needs remain underexplored. It is still unclear (1) what information users need and (2) how it should be effectively and accessibly represented. We investigate transparency needs across cognitive abilities, using Large Language Models (LLMs) to create more understandable representations of profiling. An exploratory study with 45 participants—30 without CIs and 15 with CIs—was conducted under three conditions. After 15 minutes of social media browsing, participants received either (1) a verbal explanation of profiling, (2) LLM-generated interest segments, or (3) LLM-generated user personas (in general or Easy-to-Read German for participants with CIs), followed by a semi-structured interview. Thematic analysis of transcripts revealed concerns about data sensitivity, perceived consequences, and the influence of cognitive abilities. Merely showing users collected or inferred data—regardless of format—may not meet user transparency needs. Our findings suggest transparency tools must go beyond data representation to explain inference mechanisms and potential outcomes, tailored to the sensitivities of different cognitive user groups.

Information disorder - including misinformation, disinformation, and malinformation - continues to undermine public discourse. Large language model (LLM)-based browser extensions show promise as tools for intervention, yet the role of publicly available tools in this space is not well understood. We conducted a scoping review of the Chrome Web Store and Firefox Add-Ons Store (May-July 2025), screening 951 records and including 34 extensions that use LLMs to process webpage content or user input. Through thematic analysis, we identified six interaction design concept categories: Extension Access, Display Mode, User Guidance, Customization, Content Understanding & Manipulation, and Navigation & Utilities. While extensions demonstrated diverse functionality and potential to debunk information disorder, they overwhelmingly operated as reactive tools, requiring explicit user initiation. Moreover, they rarely embedded themselves within webpage context. These findings reveal a "proactive gap" in current designs. Future LLM-powered extensions should move beyond responsiveness toward proactive, context-aware interventions that inoculate users against misinformation and strengthen critical thinking.

The increasing integration of robots into workplaces raises critical questions about human-robot synergy in interactive environments. While robots are designed to enhance productivity and safety, their successful deployment depends on effective collaboration, trust, and seamless interaction with human workers. However, existing research has primarily focused on either technical capabilities or human-centered concerns in isolation, leaving a gap in understanding how robots can be meaningfully integrated into dynamic workspaces. In this workshop, we bring together experts from robotics, HCI, and work sciences to explore the future of human-robot collaboration at the workplace. This workshop aims to identify key design principles, ethical considerations, and practical challenges. The insights gained will inform future research and policy recommendations, shaping a future in which robots act not as mere tools but as cooperative agents that enhance workplace efficiency, well-being, and innovation.

Digital assistive technologies (ATs) have been widely used to support people with disabilities at work. However, many existing systems, interfaces, and tools remain inaccessible or insufficiently adaptable to the wide range of human abilities, particularly when cognitive, communicative, or sensory differences are involved. This gap is further exacerbated by what scholars and activists refer to as the disability divide: the sociotechnical disparity between people with and without disabilities in terms of access to, use of, and benefits from digital technologies. Despite increasing policy efforts and legal frameworks, vocational inclusion and training remains a significant challenge. By bringing together a diverse community, this workshop seeks to critically examine the role of digital ATs in advancing vocational inclusion for individuals with disabilities.

The 7th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) seeks to bring together researchers from human-robot interaction (HRI), robotics, and mixed reality (MR) to address the challenges related to mixed reality interactions between humans and robots. Key topics include the development of robots capable of interacting with humans in mixed reality, the use of virtual reality for creating interactive robots, designing augmented reality interfaces for communication between humans and robots, exploring mixed reality interfaces for enhancing robot learning, comparative analysis of the capabilities and perceptions of robots and virtual agents, and sharing best design practices. VAM-HRI 2024 will build on the success of VAM-HRI workshops held from 2018 to 2023, advancing research in this specialized community. The prior year's website is located at: https://vam-hri.github.io.

With the ongoing efforts to empower people with mobility impairments and the increase in technological acceptance by the general public, assistive technologies, such as collaborative robotic arms, are gaining popularity. Yet, their widespread success is limited by usability issues, specifically the disparity between user input and software control along the autonomy continuum. To address this, shared control concepts provide opportunities to combine the targeted increase of user autonomy with a certain level of computer assistance. This paper presents the free and open-source AdaptiX XR framework for developing and evaluating shared control applications in a high-resolution simulation environment. The initial framework consists of a simulated robotic arm with an example scenario in Virtual Reality (VR), multiple standard control interfaces, and a specialized recording/replay system. AdaptiX can easily be extended for specific research needs, allowing Human-Robot Interaction (HRI) researchers to rapidly design and test novel interaction methods, intervention strategies, and multi-modal feedback techniques, without requiring an actual physical robotic arm during the early phases of ideation, prototyping, and evaluation. Also, a Robot Operating System (ROS) integration enables the controlling of a real robotic arm in a PhysicalTwin approach without any simulation-reality gap. Here, we review the capabilities and limitations of AdaptiX in detail and present three bodies of research based on the framework. AdaptiX can be accessed at https://adaptix.robot-research.de.

Robotic arms, integral in domestic care for individuals with motor impairments, enable them to perform Activities of Daily Living (ADLs) independently, reducing dependence on human caregivers. These collaborative robots require users to manage multiple Degrees-of-Freedom (DoFs) for tasks like grasping and manipulating objects. Conventional input devices, typically limited to two DoFs, necessitate frequent and complex mode switches to control individual DoFs. Modern adaptive controls with feed-forward multi-modal feedback reduce the overall task completion time, number of mode switches, and cognitive load. Despite the variety of input devices available, their effectiveness in adaptive settings with assistive robotics has yet to be thoroughly assessed. This study explores three different input devices by integrating them into an established XR framework for assistive robotics, evaluating them and providing empirical insights through a preliminary study for future developments.

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.

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.

Assistive technologies and in particular assistive robotic arms have the potential to enable people with motor impairments to live a self-determined life. More and more of these systems have become available for end users in recent years, such as the Kinova Jaco robotic arm. However, they mostly require complex manual control, which can overwhelm users. As a result, researchers have explored ways to let such robots act autonomously. However, at least for this specific group of users, such an approach has shown to be futile. Here, users want to stay in control to achieve a higher level of personal autonomy, to which an autonomous robot runs counter. In our research, we explore how Artificial Intelligence (AI) can be integrated into a shared control paradigm. In particular, we focus on the consequential requirements for the interface between human and robot and how we can keep humans in the loop while still significantly reducing the mental load and required motor skills.

Shared control in assistive robotics blends human autonomy with computer assistance, thus simplifying complex tasks for individuals with physical impairments. This study assesses an adaptive Degrees of Freedom control method specifically tailored for individuals with upper limb impairments. It employs a between-subjects analysis with 24 participants, conducting 81 trials across three distinct input devices in a realistic everyday-task setting. Given the diverse capabilities of the vulnerable target demographic and the known challenges in statistical comparisons due to individual differences, the study focuses primarily on subjective qualitative data. The results reveal consistently high success rates in trial completions, irrespective of the input device used. Participants appreciated their involvement in the research process, displayed a positive outlook, and quick adaptability to the control system. Notably, each participant effectively managed the given task within a short time frame.

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with simulated robot agents through natural language, each powered by individual GPT cores. By means of OpenAI’s function calling, we bridge the gap between unstructured natural language input and structured robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a simulated multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their simulated robot collaborators. Still, those users who did explore were able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.

Effective Human-Robot Interaction (HRI) is fundamental to seamlessly integrating robotic systems into our daily lives. However, current communication modes require additional technological interfaces, which can be cumbersome and indirect. This paper presents a novel approach, using direct motion-based communication by moving a robot's end effector. Our strategy enables users to communicate with a robot by using four distinct gestures -- two handshakes ('formal' and 'informal') and two letters ('W' and 'S'). As a proof-of-concept, we conducted a user study with 16 participants, capturing subjective experience ratings and objective data for training machine learning classifiers. Our findings show that the four different gestures performed by moving the robot's end effector can be distinguished with close to 100% accuracy. Our research offers implications for the design of future HRI interfaces, suggesting that motion-based interaction can empower human operators to communicate directly with robots, removing the necessity for additional hardware.

User-centered evaluations are a core requirement in the development of new user related technologies. However, it is often difficult to recruit sufficient participants, especially if the target population is small, particularly busy, or in some way restricted in their mobility. We bypassed these problems by conducting studies on trade fairs that were specifically designed for our target population (potentially care-receiving individuals in wheelchairs) and therefore provided our users with external incentive to attend our study. This paper presents our gathered experiences, including methodological specifications and lessons learned, and is aimed to guide other researchers with conducting similar studies. In addition, we also discuss chances generated by this unconventional study environment as well as its limitations.

Despite the growth of physically assistive robotics (PAR) research over the last decade, nearly half of PAR user studies do not involve participants with the target disabilities. There are several reasons for this -- recruitment challenges, small sample sizes, and transportation logistics -- all influenced by systemic barriers that people with disabilities face. However, it is well-established that working with end-users results in technology that better addresses their needs and integrates with their lived circumstances. In this paper, we reflect on multiple approaches we have taken to working with people with motor impairments across the design, development, and evaluation of three PAR projects: (a) assistive feeding with a robot arm; (b) assistive teleoperation with a mobile manipulator; and (c) shared control with a robot arm. We discuss these approaches to working with users along three dimensions -- individual- vs. community-level insight, logistic burden on end-users vs. researchers, and benefit to researchers vs. community -- and share recommendations for how other PAR researchers can incorporate users into their work.

Robots play a vital role in modern automation, with applications in manufacturing and healthcare. Collaborative robots integrate human and robot movements. Therefore, it is essential to ensure that interactions involve qualified, and thus identified, individuals. This study delves into a new approach: identifying individuals through robot arm movements. Different from previous methods, users guide the robot, and the robot senses the movements via joint sensors. We asked 18 participants to perform six gestures, revealing the potential use as unique behavioral traits or biometrics, achieving F1-score up to 0.87, which suggests direct robot interactions as a promising avenue for implicit and explicit user identification.