Towards a Technology-Enabled Unified Educational Space in Central Asia: A Chinese Perspective on Blockchain, Federated Learning, and Neural Machine Translation

Central Asian countries share a common Soviet educational heritage, yet their higher education systems have diverged considerably since independence in 1991. Efforts to construct a unified educational space through Bologna-style reforms have yielded limited results owing to institutional barriers, linguistic diversity, data sovereignty concerns, and administrative inefficiency in credential recognition. This paper proposes a technology-enabled framework for educational integration in Central Asia, leveraging three emerging technologies: blockchain for credential verification and credit transfer, federated learning for privacy-preserving cross-border data collaboration, and neural machine translation for overcoming language barriers. Unlike conventional approaches that demand institutional harmonization, the proposed framework prioritizes functional interoperability while respecting national sovereignty. From a Chinese perspective, the framework embodies principles of South–South cooperation and technological enablement through the Belt and Road Initiative. We describe the architecture of each technological layer, propose a phased implementation strategy with concrete pilot projects, and validate the framework through semi-structured interviews with eight educational administrators and IT specialists across three Central Asian countries. The expert validation confirms the framework's practical relevance while highlighting infrastructure readiness and regulatory adaptation as critical preconditions. This study contributes to debates on alternative models of educational regionalization beyond Western-centric frameworks and offers practical guidance for policymakers, educational institutions, and technology developers.