Noospheric Technologies in Knowledge-Intensive Socio-Technical Systems: A Conceptual Framework Applied to Science and Education

Digital transformation has expanded data infrastructures, artificial intelligence (AI)-supported systems, and automation across knowledge-intensive domains. However, these developments often remain fragmented and are not consistently aligned with human well-being, epistemic integrity, or collective learning. This conceptual and integrative review develops a bounded multi-layer framework of noospheric technologies for examining the alignment of data infrastructures, AI-supported cognition, governance arrangements, and human capabilities in knowledge-intensive systems. Noospheric technologies are defined as a contemporary extension of the established noosphere concept, referring to socio-technical configurations that support collective knowledge production and socially accountable development. The framework is applied to scientific and educational systems as two focal domains. In science, the review examines Findable, Accessible, Interoperable, and Reusable (FAIR) research data environments, AI-assisted discovery, and automated research workflows. In education, it examines adaptive learning ecosystems, learning analytics, and AI-supported educational infrastructures. The synthesis identifies recurring constraints across the two domains, including data fragmentation, limited interoperability, opaque AI-mediated reasoning, algorithmic bias, incentive misalignment, privacy and governance challenges, and uneven capability development. The review translates the framework into observable mechanisms, risks, and evaluation criteria for future research, policy analysis, and institutional practice.