Applying LLMs to Legacy System Modernization in Higher Education IT: Leveraging Large Language Models Beyond Chatbots to Modernize Core Student and Administrative Systems in Universities—A Suggestive Review Study

Mahesh Kumar Damarched ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 01, 3043-3061

Abstract: Higher education institutions tend to manage decades-old legacy systems including mainframes, COBOL-based Student Information Systems (SIS) and PeopleSoft Enterprise Resource Planning (ERP) platforms that account for 60–80% of the IT budget, while simultaneously implementing artificial intelligence for student-facing experiences. This review studies the unexplored potential of Large Language Models (LLMs) as “intelligent copilots†for thorough legacy system modernization across the full lifecycle in higher education IT, including assessment, documentation, code translation, refactoring, testing, and optimization processes. We advocate that the actual leverage is found at the intersection of “LLMs in education†and “LLMs for code modernization†, a convergence that has not been explored in the published researches and has been visualized separately till now, by synthesizing recent literature (2023–2025) on LLM-enabled reverse engineering, code generation and documentation automation. The current modernization efforts tailored to higher education such as AI Virtual Explorer for Research Discovery and Education (AI-VERDE), FernUni LLM Experimental Infrastructure (FLEXI), and other institutional AI gateways are also reviewed in this study. This review study suggests an end-to-end reference architecture that combines multi-agent workflows, Continuous Integration and Continuous Delivery/Deployment (CI/CD) validation, and Retrieval-Augmented Generation (RAG). Numerous studies show that LLMassisted modernization results in 35–40% cost savings and 50% timeline reductions allowing institutions to shift resources from maintenance to innovation. In order to unlock untapped technical value and simultaneously empower contemporary student and administrative experiences this review suggests positioning the LLMs as strategic enablers of dual transformation rather than just productivity tools for educators. By using this integrated approach universities can create sustainable digital ecosystems operational resilience and an unparalleled competitive advantage.

Keywords: Large Language Models; Legacy System Modernization; Higher Education IT; Student Information Systems; LLMEnabled Code Transformation; AI Copilots; Digital Transformation in Universities. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:01:ijisrt26jan1243

DOI: 10.38124/ijisrt/26jan1243

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