AI-Driven Content Intelligence in Higher Education: Transforming Institutional Knowledge Management

Abstract

Higher education institutions generate vast and diverse volumes of digital content, including academic resources, administrative records, research outputs, and regulatory documentation. Managing this heterogeneous and largely unstructured information remains a significant challenge for traditional knowledge management systems, which primarily focus on storage and keyword-based retrieval with limited semantic understanding. This paper presents an AI-driven content intelligence approach aimed at transforming institutional knowledge management in higher education. The proposed framework integrates machine learning, natural language processing, and semantic technologies to automate content ingestion, classification, enrichment, and governance across the institutional content lifecycle. Core capabilities such as semantic extraction, topic modeling, document classification, named entity recognition, and knowledge graph construction enable deeper contextual understanding and intelligent discovery of institutional knowledge assets.