Cloud-Native Technology-Enabled Maturity Evolution of University IT Service Management for AI in Education Scenarios

Abstract

The rapid advancement of Artificial Intelligence in Education (AIEd) has posed unprecedented challenges to university IT service management, particularly in elastic resource scheduling, heterogeneous system collaboration, and intelligent service responsiveness. Cloud-native technologies, characterized by containerization, microservices, and automated orchestration, provide a transformative solution for supporting large-scale AI teaching platforms, intelligent research analytics, and personalized learning services in higher education [1,2]. This study develops a maturity evaluation model for university IT service management in cloud-native environments, categorizing maturity into five levels (Initial, Basic, Comprehensive, Excellent, Outstanding) across four domains (Infrastructure, Application Development, Service Governance, Operation & Maintenance) with twelve core processes. Through comparative analysis of containerization, microservices, and Kubernetes in educational contexts, a three-stage evolution pathway ("Technology Adaptation - Process Reengineering - Organizational Collaboration") is proposed. Empirical validation from three universities with diverse profiles confirms the model's applicability. The research reveals that institutions should adopt differentiated technical routes based on AIEd scenario requirements: resource-constrained universities prioritize containerization for AI experimental teaching, while technically advanced institutions leverage Kubernetes for hybrid cloud AI computing platforms. This work contributes a practical maturity assessment tool and implementation guidelines for AIEd adoption in university digital transformation [3,4].