Sustainability

This mixed-methods study examines the digital competence of primary school teachers in Greece and Cyprus working in multilingual and multicultural mainstream classrooms. In response to the increasing diversity in European education, it explores how teachers perceive and implement digital competence to support inclusive and quality education. Using the DigCompEdu framework and an extended TPACK model, data were collected from 146 in-service teachers through a structured questionnaire. Cluster analysis revealed three distinct competence profiles-high, moderate, and low-while Kruskal–Wallis tests confirmed significant differences among them. Thematic analysis of open-ended responses, supported by Pearson correlation analysis, highlighted how teachers’ beliefs, infrastructural conditions, and pedagogical practices intersect. Highly competent teachers reported the use of inclusive digital strategies yet pointed to systemic barriers such as limited training and poor infrastructure. Less confident teachers expressed foundational challenges and dependence on external support. Across all profiles, contextual factors—school resources, time, student digital readiness, and access to professional development—were key. The study concludes that digital competence is not merely technical but deeply context-sensitive and pedagogical. It calls for differentiated, equity-oriented professional learning pathways aligned with Sustainable Development Goals 4 and 10, contributing to inclusive education and education for sustainability in linguistically diverse classrooms.

Providing effective post-disaster housing remains a globally complex challenge shaped by interrelated constraints, including environmental sustainability, socio-cultural compatibility, logistical capacity, and economic feasibility. Contemporary responses therefore require housing solutions that extend beyond rapid deployment to incorporate flexibility, adaptability, and long-term spatial transformation. In this context, this study advances a design-oriented, computational framework that positions parametric design at the core of post-disaster housing production within the broader digital transformation of the construction sector. The research proposes an adaptive parametric–modular housing system in which standardized architectural units are governed by a rule-based aggregation logic capable of generating context-responsive spatial configurations across multiple scales and typologies. The methodology integrates a qualitative synthesis of global post-disaster housing literature with a quantitative computational workflow developed in Grasshopper for Rhinoceros 3D (version 8). Algorithmic scripting defines a standardized spatial grid and parametrically regulates key building components structural systems, façade assemblies, and site-specific environmental parameters, enabling real-time configuration, customization, and optimization of housing units in response to diverse user needs and varying climatic, social, and economic conditions while maintaining constructability. The applicability of the framework is examined through a case study of the Düzce Permanent Housing context, where limitations of existing post-disaster stock, such as spatial rigidity, restricted growth capacity, and fragmented public-space integration, are contrasted with alternative settlement scenarios generated by the proposed system. The findings demonstrate that the framework supports multi-scalar and multi-typological reconstruction, extending beyond individual dwellings to include public, service, and open-space components. Overall, the study contributes a transferable computational methodology that integrates modular standardization with configurational diversity and user-driven adaptability, offering a sustainable pathway for transforming temporary post-disaster shelters into permanent, resilient, and socially integrated community assets.

The article addresses the problem of the insufficient empirical recognition of the determinants of trust in artificial intelligence (AI) algorithms in organizations operating under conditions of sustainable development. The aim of the study was to identify the factors shaping organizational trust in AI and to examine how perceived trustworthiness, transparency, and effectiveness of algorithms influence their acceptance in the work environment. The research was conducted using a quantitative survey-based approach among organizational employees, which enabled the analysis of relationships between key variables and the identification of factors that strengthen or limit trust. The results indicate that algorithmic transparency, the reliability of generated outcomes, and the perceived effectiveness of AI applications significantly foster trust, whereas concerns related to errors and the decision-making autonomy of systems constitute important barriers to acceptance. Based on the findings, a conceptual and exploratory model of trust in AI was proposed, which may be used to diagnose the level of technology acceptance and to support the responsible implementation of artificial intelligence-based solutions in organizations. The contribution of the article lies in integrating organizational and technological perspectives and in providing an empirical approach to trust in AI within the context of sustainable development.