Abstract:

Recent research on digital education has found that Learning Management Systems (LMS), including common platforms like Moodle, continue to be content-focused, offering little support in intelligent processing of knowledge and workflow oriented to instructor needs. Existing LMS implementations lack semantic content organization, automated knowledge extraction, and efficient retrieval mechanisms, which leads to more manual work for instructors and administrators. Additionally, contemporary approaches to leveraging AI in educational settings tend toward fragmented and task-specific applications, with no unified and scalable solution available for intelligent management of content in LMS environments.

To address these limitations, this paper proposes an AI-driven framework for intelligent content processing and retrieval in LMS platforms. We adopt a modular, plugin-based architecture that enables integration without requiring modifications to core LMS systems, ensuring scalability and ease of deployment.

The proposed system incorporates automated AI pipelines to process multi-format educational resources, transforming different kinds of content into transcripts, summaries, questionnaires, and semantically enriched representations. These representations are encoded and then stored in a vector-based database to support efficient similarity-based retrieval. Furthermore, retrieval-augmented generation (RAG) mechanisms are incorporated to provide context-aware access to course-specific knowledge and to enable automated generation of assessment content and structured learning artifacts.

By facilitating efficient access, organization, and reuse of learning resources, the framework reduces manual effort and enhances instructor workflows. It also supports improved course design and enables scalable content governance from an administrative perspective.

This work presents a scalable and extensible AI framework that integrates content processing, semantic retrieval, and RAG-based knowledge access to support instructor-centric workflows in LMS environments.

Keywords:

AI, Learning Management Systems, Retrieval-Augmented Generation, Semantic Retrieval, Content Processing, Instructor Support, Intelligent Content Management.