CONCEPTUAL FRAMEWORK FOR AUTOMATED CURRICULUM MAPPING IN OUTCOME-BASED EDUCATION USING SEMANTIC REASONING

Abstract

Higher education institutions globally are being mandated to implement Outcome-Based Education (OBE), placing curriculum mapping at the center of quality assurance. Yet mapping remains labour-intensive, subjective, and inadequate at scale. This paper proposes the Semantic-AI Curriculum Mapping (SACM) Framework—a conceptual architecture integrating ontologies, knowledge graphs, semantic reasoning, and Large Language Models (LLMs) to automate OBE-aligned curriculum mapping. A purposive synthesis of 30 peer-reviewed publications (2021–2026) is used to derive a six-category problem taxonomy and a six-layer framework, validated through a traceability matrix. No implementation is presented. Three original contributions are advanced: a problem taxonomy, the six-layer SACM framework, and a traceability matrix demonstrating comprehensive coverage of identified barriers. The framework's modular design is contextualized for Nigerian universities under NUC's Core Curriculum and Minimum Academic Standards (CCMAS, 2022).