Open Access Articles

AI assistance can be seen as a welcome aid in radiology, a highly complex environment characterized by round-the-clock time pressure and quality expectations. However, it must meet high ethical standards from the perspective of both users and patients. It is a challenge to incorporate this human-centered approach into the development and introduction of AI applications.

Artificial intelligence (AI) is changing the world of work. But how can work teams learn to use AI support in a way that delivers speed advantages and ensures consistently high quality? One possible approach is to test it in a workplace-like simulation. Trying it out under realistic conditions shows the role that critical thinking plays.

Designing adaptations in AI-based work systems poses a central challenge for achieving human-centered AI (HCAI). This paper presents a methodical approach that enables transdisciplinary teams to systematically explore and structure the design space of adaptable work systems. Building on an extended work system model and operationalized through a matrix-based framework, the method supports the identification of interdependencies, stakeholder perspectives, and context-specific goals. Its practical applicability is demonstrated through a real-world case study in radiographic non-destructive testing.

AI opens up entirely new forms of value creation, but most business model tools have not kept pace. They overlook both the strategic potential that AI holds and the ethical challenges that it introduces. This study introduces a dual-card toolkit that helps interdisciplinary teams design AI-enabled business models with built-in ethical reflection. The key insight: to harness AI responsibly, we must rethink how we innovate, starting from the business model itself.

In-orbit servicing of satellites presents several challenges as the satellite hardware is exposed to external influences throughout its life cycle. These factors wear down the components and cause changes to their physical structure. In such cases, the limits of simple dis- and reassembly steps may be reached, as the gripping surfaces are no longer present or suitable. This paper proposes an approach of an adaptive grip position estimation in a CubeSat disassembly process. The relevant components are identified using CAD models and a 3D camera. The gripping positions are determined based on the geometry of the gripper and the point cloud of the component.

Demographic change is exacerbating the shortage of labor and skilled workers in the manufacturing industry, making knowledge management an increasingly important issue in many companies. Collecting and preserving tacit knowledge poses a particular challenge. This study examines the extent to which large language models (LLMs) can provide meaningful support in knowledge gathering through expert interviews. Three experts test and evaluate a personalized chatbot that has been developed using ChatGPT-5. The results of the interview are promising, but the summary shows room for improvement.

The paper specifies the genAI support needs for industrial maintenance against the background of a sociotechnical systems perspective. Emphasizing two needs, accessing implicit operator knowledge and prioritizing complex regulatory knowledge, a multi-layer architecture is outlined for an AI-based context-sensitive maintenance assistance system (MAS). The main purpose is to bridge knowledge gaps with genAI if human expertise and human implicit knowledge are not available and to cope with sub-process-specific challenges of multiple regulations. The MAS facilitates access to technical knowledge, distributes expertise, and shares implicit knowledge of experienced operators across different layers of information processing. The approach goes beyond standardization and has a high potential to enhance organizational as well as individual resilience.

The rehumanization of industrial work has emerged as a key focus in Industry 4.0 research, emphasizing the empowerment of human workers amidst advancing automation. Within this re-search, supporting workers with disabilities through digital assistance technologies serves as a prime example of a human-centric approach to industrial engineering. These technologies often claim to enhance productivity, which aims to promote the integration of workers with disabili-ties in industrial roles. But can they genuinely improve their work experience? This ethnograph-ic study presents insights from two years of developing and deploying augmented reality in a sheltered woodworking workshop. Over this period, we engaged in conversations and facilitat-ed over 30 technology sessions with workers with diverse disabilities. Our experiences chal-lenge the narrative of industrial research, in particular with digital instructional systems serving as “enabler technology” to help them work “better.” ...

The increasing complexity of industrial environments demands new competencies from workers, particularly the ability to interact with advanced digital systems. Traditional training methods often fall short in supporting the effective transfer of applied knowledge to such contexts, and the effectiveness of this transfer, as measured by performance-based outcomes, remains to be investigated. To address this gap, the present study employed a between-subjects experimental design comparing augmented reality- and paper-based instructions within a realistic production training scenario. The results show that participants who learned with augmented reality completed the production process significantly faster and with fewer errors than those using paper instructions. In addition, learners using augmented reality reported higher usability and experienced lower cognitive load during training. These findings suggest that augmented reality can enhance the transfer of practical skills in industrial ...

The engineer-to-order (ETO) production approach is gaining relevance in response to increasing demand for individualized products and small batch sizes. However, ETO inherently reduces the economies of scale typically achieved in series production, as each order requires tailored engineering and production steps. This loss of efficiency can be mitigated through demand-driven and context-aware information provision throughout the product development process. A recommendation system based on semantic artificial intelligence (AI) and machine learning can support this by i) analyzing historical data and prior knowledge, for example drawings or a bill of materials from previous projects, and ii) making automated suggestions, like reusing existing designs or proposing design alternatives, thus compensating for the aforementioned effects.