In an era marked by accelerated technological transformations, education is undergoing a process of metamorphosis that fundamentally redefines its current structure. The objectives of the present research are oriented toward analysing how participants in the educational process, teachers and learners, are facing new challenges and limitations. The article explores the relevant literature to identify the obstacles and inefficiencies of the educational process and examines how emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain can contribute to overcoming these barriers in the context of an ongoing technological revolution. The study aims to provide an in-depth understanding of how these technologies can support educational activities (teaching–learning–assessment) and the adaptation of education to ongoing transformations, thereby improving educational performance and shaping the future of the educational system.
The rapid evolution of recruitment processes has prompted organizations to explore advanced technologies that enhance efficiency, transparency, and fairness in talent acquisition. This review critically examines the integration of Blockchain and Machine Learning (ML) as transformative tools in addressing long-standing challenges such as credential fraud, skills mismatch, unconscious bias, and lack of process transparency. Blockchain provides a decentralized, immutable ledger for secure storage and verification of academic and professional credentials, thereby reducing fraudulent claims and streamlining verification through smart contracts. The convergence of these technologies creates robust frameworks where ML algorithms operate on authenticated data supplied by blockchain systems, reinforcing trust, accountability, and efficiency in recruitment workflows. This study highlights both the transformative opportunities and practical challenges of these technologies, providing insights for academia, practitioners, and policymakers to design sustainable recruitment strategies aligned with modern workforce demands.
Quantum Key Distribution (QKD) provides information-theoretic security based on quantum mechanics, but its practical deployment is limited by its reliance on classical infrastructure and inability to operate as a standalone solution. Recent research emphasizes hybrid approaches that combine QKD with post-quantum cryptography (PQC) to achieve practical and robust security. This paper presents a hybrid key distribution model integrating QKD with the post-quantum key encapsulation mechanism. The two independently generated keys are combined to derive a unified shared secret, enhancing resilience against both classical and quantum adversaries. The system is implemented using a simulated quantum environment, enabling the emulation of quantum communication over classical networks. The proposed approach is validated through a secure image transmission use case, demonstrating the correctness of the hybrid key exchange. The results highlight the feasibility of hybrid classical–quantum cryptographic systems as a practical step toward secure communication in emerging quantum network infrastructures.
Housing affordability remains a major concern for young Europeans, as growing real-estate prices and limited financial accessibility increasingly challenge their capacity to purchase or rent a home. This paper combines quantitative analysis with digital innovation to explore and visualize the determinants of housing affordability across EU member states. Using Eurostat and World Bank datasets, we perform statistical modeling to identify the key factors influencing young adults’ ability to access housing-such as income, mortgage availability, employment stability, and governmental support mechanisms. Beyond the empirical findings, we propose a mobile application with an interactive map designed to make the analysis more accessible to the public. The platform enables users to explore housing costs across Europe, visualize affordability differences through dynamic charts, and complete a short questionnaire that suggests EU countries best aligned with their income, openness to mobility, and occupational profile. This integrated approach bridges data science and decision support, offering both researchers and citizens a tool to better understand and navigate the European housing market.
This paper explores the paradigm shift in software engineering by positioning Artificial Intelligence (AI) as an active co-creator within the Software Development Life Cycle (SDLC). While traditional AI tools function as passive assistants, the proposed framework integrates large language models and predictive analytics to foster a collaborative environment between human developers and machine intelligence. Through a Retrieval-Augmented Generation (RAG) approach, the study evaluates improvements in requirements analysis, code generation, and proactive defect detection. Experimental simulations indicate a 57% reduction in lead time and a 37% decrease in defect density, highlighting significant productivity gains. However, the research also identifies the "Reviewer’s Paradox," where the cognitive load shifts toward verification and architectural oversight. The findings suggest that AI-human co-creation not only optimizes resource allocation but also necessitates a fundamental redefinition of developer roles in the era of AI-native software engineering.
