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. CCS Concepts• Human-

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.

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.

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.

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.

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot’s motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.

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.

Robotic solutions, in particular robotic arms, are becoming more frequently deployed for close collaboration with humans, for example in manufacturing or domestic care environments. These robotic arms require the user to control several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving grasping and manipulating objects. Standard input devices predominantly have two DoFs, requiring time-consuming and cognitively demanding mode switches to select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have shown to decrease the necessary number of mode switches but were up to now not able to significantly reduce the perceived workload. Users still bear the mental workload of incorporating abstract mode switching into their workflow. We address this by providing feed-forward multimodal feedback using updated recommendations of ADMC, allowing users to visually compare the current and the suggested mapping in real-time. We contrast the effectiveness of two new approaches that a) continuously recommend updated DoF combinations or b) use discrete thresholds between current robot movements and new recommendations. Both are compared in a Virtual Reality (VR) in-person study against a classic control method. Significant results for lowered task completion time, fewer mode switches, and reduced perceived workload conclusively establish that in combination with feedforward, ADMC methods can indeed outperform classic mode switching. A lack of apparent quantitative differences between Continuous and Threshold reveals the importance of user-centered customization options. Including these implications in the development process will improve usability, which is essential for successfully implementing robotic technologies with high user acceptance.

Nowadays, robots collaborate closely with humans in a growing number of areas. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, supporting people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior. This, however, is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their motion intent and comprehending how they ”think” about their actions. We work on solutions that communicate the cobots AI-generated motion intent to a human collaborator. Effective communication enables users to proceed with the most suitable option. We present a design exploration with different visualization techniques to optimize this user understanding, ideally resulting in increased safety and end-user acceptance.

Assistive technologies such as human-robot collaboration, have the potential to ease the life of people with physical mobility impairments in social and economic activities. Currently, this group of people has lower rates of economic participation, due to the lack of adequate environments adapted to their capabilities. We take a closer look at the needs and preferences of people with physical mobility impairments in a human-robot cooperative environment at the workplace. Specifically, we aim to design how to control a robotic arm in manufacturing tasks for people with physical mobility impairments. We present a case study of a sheltered-workshop as a prototype for an institution that employs people with disabilities in manufacturing jobs. Here, we collected data of potential end-users with physical mobility impairments, social workers, and supervisors using a participatory design technique (Future-Workshop). These stakeholders were divided into two groups, primary (end-users) and secondary users (social workers, supervisors), which were run across two separate sessions. The gathered information was analyzed using thematic analysis to reveal underlying themes across stakeholders. We identified concepts that highlight underlying concerns related to the robot fitting in the social and organizational structure, human-robot synergy, and human-robot problem management. In this paper, we present our findings and discuss the implications of each theme when shaping an inclusive human-robot cooperative workstation for people with physical mobility impairments.

Virtual worlds are infinite environments in which the user can move around freely. When shifting from controller-based movement to regular walking as an input, the limitation of the real world also limits the virtual world. Tackling this challenge, we propose the use of electrical muscle stimulation to limit the necessary real-world space to create an unlimited walking experience. We thereby actuate the users` legs in a way that they deviate from their straight route and thus, walk in circles in the real world while still walking straight in the virtual world. We report on a study comparing this approach to vision shift - the state of the art approach - as well as combining both approaches. The results show that particularly combining both approaches yield high potential to create an infinite walking experience.