From Learning Management Systems to Adaptive Learning Systems: A Systematic Architectural Transformation Framework - Sartorio, Alejandro; Rossi, Gustavo (Anais do Congresso Ibero-Americano em Engenharia de Software (CIbSE); 2026: Anais do XXIX Congresso Ibero-Americano em Engenharia de SoftwareDO - 10.5753/cibse.2026.42474, 2026)
Traditional Learning Management Systems (LMS) predominantly operate as static content repositories under a ”one-size-fits-all” paradigm, failing to address the diverse cognitive needs and learning paces of individual students. While Adaptive Learning Systems (ALS) offer a solution through personalization, migrating legacy monolithic LMS infrastructures involves overcoming significant technical debt and ”architectural stiffness.” This paper proposes ARALS (Adaptive Reference Architecture for Learning Systems), a framework designed to evolve legacy LMSs into genuine adaptive environments without requiring a complete codebase rewrite. To this end, we introduce a Conceptual Transformation Sequence (C1) and the PALS framework (Properties of Adaptive Learning Systems) as a systematic method to inject and validate adaptivity. ARALS integrates the MAPE-K autonomic computing cycle into a decoupled, seven/eight-layer structure, acting as a control mechanism for the operational flow to ensure pedagogical adaptation. The proposal is validated through a high-fidelity case study in which the Odoo eLearning module is architecturally refactored. Results demonstrate the successful implementation of dynamic ”Learning Roadmaps” and personalized content sequencing with negligible performance overhead, proving the viability of ARALS as an interdisciplinary bridge between software engineering and adaptive pedagogy.