Abstract— Industry 4.0 and the emerging Industry 5.0 paradigm are reshaping industrial value chains, and aviation as a safety-critical, heavily regulated sector faces distinctive complications in pursuing digital transformation. Airlines, MRO organizations, airports, OEMs, and suppliers must meet demanding requirements for certification readiness, traceability, cybersecurity, and human oversight that exceed the demands of generic Industry 4.0 adoption. Consequently, Digital Transformation Centers (DTCs) need to adjust their strategic roadmap to serve as capable intermediaries that translate transformation ambitions into compliant, operationally grounded outcomes. This paper addresses the question: what capability requirements must DTCs possess to support aviation stakeholders in achieving Industry 4.0 and Industry 5.0 transformation? To answer this, an AI-assisted literature review, bibliometric science mapping, and comparison of leading DTC archetypes are combined to capture capability dimensions within structured frameworks and rank requirements using a composite priority index. The analysis yields a ten-part capability framework covering technological, organizational, human-centric, assurance, and ecosystem dimensions. The top five ranked requirements are advanced technology capability, human capital and training, strategic and organizational capability, cybersecurity and data governance, and quality, traceability, and certification readiness with priority scores of 4.33, 4.33, 4.33, 4.26, and 4.22, respectively. These results underline that DTCs must grow their capabilities beyond production and enterprise-level management to encompass organizational transformation, data governance, and quality assurance functions in order to remain sectorally relevant and practically credible in aviation.

Due to the dynamism of the current AI development, the transformative potential of Generative Artificial Intelligence (GenAI) within Industry 5.0 remains underexplored, particularly in the context of sustainable smart manufacturing. This study investigates how GenAI-supported product development affects key Industry 5.0 principles of human-centricity, resilience, and sustainability across traditional phases of the product lifecycle. Drawing on sociotechnical systems (STS) theory and a multi-level analytical framework, the paper conceptualises GenAI as an overarching process support layer connecting the micro (shop-floor), meso (process), and macro (organisational) levels of manufacturing. A scoping review is employed to map the evolving research landscape, and to identify conceptual as well as empirical gaps. The analysis highlights how GenAI applications may enhance sustainability by promoting resource efficiency, emission reduction and circular value creation, while simultaneously raising challenges for human agency, transparency, and workforce adaptation. The findings contribute theoretically by integrating STS and Industry 5.0 paradigms, methodologically by developing a structuring multi-level analytical grid, and practically by offering a structured foundation for embedding GenAI strategically into sustainable product development. Overall, the paper aims at a holistic understanding of GenAI as a sociotechnical enabler of industrial transformation toward human-centric, resilient, and sustainable manufacturing systems.

Virtual Reality has gained relevance in higher education due to its potential to enhance traditional teaching methods. However, lecturers struggle to understand which learning problems VR addresses. This study asks which problems VR addresses and how these problems and related VR use cases can be structured systematically. Based on a systematic literature review of 48 peer-reviewed studies, we identified recurring problem and solution dimensions and linked them through learning objectives. The result is a VR problem space that instantiates the concept of a problem space using the Problem Space Mapping Framework. It supports lecturers in exploring VR use cases based on learning problems and, vice versa, in identifying which problems are addressed by specific VR use cases.

Higher Education Institutions (HEIs) increasingly rely on Digital Transformation (DX) to maintain competitiveness and foster Data-Driven Decision Making (DDDM) in their administrative processes. However, academic managers frequently face operational bottlenecks when attempting to align local curricula with standardized national assessments, such as the Brazilian National Student Performance Exam (ENADE). Because data is often isolated in fragmented or manual systems, tracking the effectiveness of a curriculum against national benchmarks remains a significant challenge. To bridge this gap, this paper introduces eXA, a lightweight, web-based platform developed to integrate continuous curriculum management with historical assessment data. Built upon a modern architectural stack utilizing Python, Flask, and a flexible NoSQL database (MongoDB), the system features a server-side visual analytics engine that generates real-time benchmarking dashboards. The deployment of eXA demonstrates that automating the integration of educational data accelerates the DIKW (Data, Information, Knowledge, Wisdom) hierarchy and facilitates the Plan-Do-Check-Act (PDCA) cycle. By reducing administrative friction and providing clear, visual insights, the platform enables coordinators to swiftly identify curricular gaps and implement targeted pedagogical interventions. Ultimately, this case study illustrates how agile digital platforms enhance organizational agility and strategic competitiveness in higher education management.