Despite growing scholarly attention, the concept of a human-centered metaverse remains insufficiently defined and operationalized in current research and practice. Positioned within ongoing shifts toward user-centricity, explainability, and responsible innovation, this paper conceptualizes the human-centered metaverse as an immersive ecosystem that prioritizes user agency, inclusivity, and ethical interaction. Drawing on advances in AI-driven personalization and extended reality (XR), it derives actionable design requirements to address accessibility, cognitive load, and stakeholder collaboration in metaverse-based services. A supporting toolbox, developed at Fraunhofer IAO’s Research and Innovation Center for Cognitive Service Systems (KODIS) within the INSTANCE project, enables systematic service development from ideation to rollout. It recommends pre-evaluated tools that follow human-centered principles, usability, and ethical design standards, reducing entry barriers and supporting co-creative design. The paper establishes foundational guidelines and practical instruments for implementing responsible, user-centric services in the metaverse, demonstrated through industrial safety training scenarios that connect cognitive digital twins with real-world relevance.

The increasing complexity of data platform environments has amplified the need for transparent, structured, and reproducible software selection processes. This paper investigates under which conditions a decision support system (DSS) becomes a meaningful and effective alternative to traditional, expert-based consulting practices. To address this question, the study combines a systematic narrative literature review with a comparative evaluation of two empirical investigations: an assessment of individualized consulting approaches and an evaluation of a structured, GenAI-enabled DSS based on the Analytic Hierarchy Process (AHP). The literature review synthesizes key criteria, methodological challenges, and existing decision-making frameworks in data platform selection, revealing longstanding gaps in methodological rigor and knowledge accessibility. The empirical comparison demonstrates that DSS-supported selection improves documentation quality, process transparency, reproducibility, efficiency, and—most notably—decision consistency. By identifying the threshold at which structured, AI-enhanced decision support proves advantageous, this study contributes to a clearer understanding of when a DSS becomes economically and operationally justified. The findings highlight the shift from person-dependent decision processes to system-supported governance models and underscore the continuing relevance of human expertise within hybrid human–AI evaluation frameworks.

3D printing (3DP) has been implemented to varying degrees in many industries including aerospace, automotive, consumer goods, and perhaps most successfully in healthcare devices (especially in personalised hearing aids where it has become the standard manufacturing method). To date this digital transformation has had very little impact on pharmaceutical drug manufacturing. The industry is notoriously conservative, with many bureaucratic hurdles, however the potential advantages of personalised, point-of-care printing of pharma drugs mean that the supply chains and business models for this to happen at scale are now being explored more seriously. Hence, this exploratory research investigates and evaluates current developments using a PESTEL framework to summarise the outputs. Our results indicate that pharma 3DP, while still in the early stages of development, has clear potential to offer substantial advantages over existing methods in this important industry.

As the digitization of production and the use of Open Platform Communications Unified Architecture (OPC UA) become more prevalent, making informed decisions about extent and subsequently effort when implementing OPC UA models requires a baseline. This evaluation compares 100 OPC UA Companion Specifications (CS) regarding their size, the intent of usage of attributes and Conformance Units and their dependency hierarchies. Averages of node counts are given, the number of aggregated nodes, children in inheritance hierarchies and method arguments are evaluated. The node attributes WriteMask, RolePermissions and AccessRestrictions turn out not to be used in CS, while AccessLevel, EventNotifier, Value and Description seem to be used with varying intentions. Conformance Units are included in the CS models, from their use, too, different usage patterns can be concluded. This work can be used as a baseline to compare OPC UA type models to, a reference what extent of elements to expect from CS and a representation of the status quo of OPC UA CS today.

This paper examines how AI-driven generative models can accelerate 3D asset creation for immersive service development in Metaverse-based environments. It employs a qualitative, exploratory approach that combines a structured literature review, comparative analysis of representative tools, and selected use cases from healthcare, retail, culture, gaming, and logistics. The results indicate that generative models such as GANs, Gaussian Splatting, diffusion models, and NeRFs significantly reduce production time, enable scalable asset variation, and lower dependence on specialized 3D expertise, thereby supporting rapid prototyping and iterative, user-centered service design. At the same time, the study identifies key limitations related to fine-grained control, dataset bias, pipeline integration, intellectual property concerns, and computational requirements. The paper concludes that generative AI is a critical enabler for rapid and democratized 3D asset creation in immersive services, while underscoring the need for improved controllability, standardized workflows, and governance frameworks for broader adoption.