A Proposed AI-Base Framework for Supporting LMS Cloud Migration Decisions

Abstract

Educational institutions are increasingly confronted with the problems associated with locally hosted LMS infrastructure, including scalability issues, security risks, maintenance costs, and other challenges. Despite the considerable body of research on cloud adoption, readiness, and legacy systems migration, these issues are usually considered separately and do not provide sufficient support to educational institutions' decisions concerning the appropriateness of LMS migration to the cloud and its scope. In this regard, the study suggests an AI-Assisted LMS Cloud Migration Decision Framework, which combines institutional assessment before migration and AI-assisted analysis and recommendations on migration. Technical, operational, security, financial, and organizational factors are considered in the framework and used for assessing the possibility of migration and its appropriate scope for the institution based on these factors. Contrary to the assumption about the universal suitability of cloud migration, the suggested framework allows for considering three major migration alternatives: full, partial, or hybrid migration, as well as non-migration of the institution's current environment, in case of its unsuitability. AI is used in the process as a decision-making tool instead of a decision maker, and the ultimate decision is left to the institution itself.