European International Journal of Multidisciplinary Research and Management Studies

Cross-Domain AI Evolution: A Framework for Capability Transformation and Value Enhancement

2026-10-01T04:58:02+00:00

Artificial intelligence (AI) is increasingly evolving from domain-specific automation toward reusable capabilities that can be transferred, recombined, governed, and commercialized across heterogeneous application environments. However, cross-domain AI adoption is not equivalent to simply migrating an existing model from one sector to another. Effective migration requires adaptation of technical capabilities, data structures, governance mechanisms, incentives, security controls, and value-generation processes. This research develops a conceptual framework for understanding cross-domain AI evolution as a progression from capability transfer to capability transformation and ultimately to value enhancement. The framework synthesizes evidence from studies addressing AI-enabled medical image segmentation, unsupervised learning, financial forecasting, ESG-related financial analysis, privacy-preserving digital commerce, AI commercialization, and AI-driven cybersecurity. The methodology employs comparative thematic synthesis and architectural abstraction to identify reusable capability layers and transformation mechanisms across domains. The analysis indicates that cross-domain AI value depends on four interdependent dimensions: capability portability, contextual adaptation, trustworthy governance, and measurable value realization. The resulting framework positions AI evolution as a continuous process in which technical capabilities are transferred, contextualized, integrated with domain constraints, and converted into economic, operational, scientific, or social value. The study contributes a structured theoretical model for researchers and practitioners seeking to design scalable AI systems capable of operating beyond their original application boundaries.

Pedagogical Conditions for Improving the Professional Readiness of Teaching Staff Through Smart Technologies

2026-10-16T12:40:42+00:00

This article analyzes the development of teachers’ readiness for professional practice through SMART technologies on the basis of dissertation materials. It elucidates the relationships among the methodological approach of the study, digital and methodological competence, interactive learning activities, differentiated and reflective assessment, components of a positive psychological and pedagogical environment, and assessment criteria. The analysis concerns the aggregated findings reported in the dissertation rather than unpublished primary data from the experiment involving 323 teachers.

Types and Linguistic Features of Texts Posing A Threat to Sociopolitical Security

2026-10-27T13:01:06+00:00

This article examines manipulation and the linguistic features associated with texts that promote national, racial, or religious hostility or contain messages directed against the constitutional order. The study adopts a qualitative, conceptual approach, drawing on the cited literature and combining conceptual analysis with lexical-semantic and pragmatic interpretation. The discussion identifies several potentially relevant indicators, including negative group stereotypes, assertions of inherent superiority, attribution of hostile intentions, conspiracy allegations, and explicit or implicit calls for discriminatory or violent action.