Aug 2026· Global Journal of Engineering and Technology Advances· Vol 28, pp. 121-128· 0 citations
TL;DR
The Semantic-AI Curriculum Mapping (SACM) Framework is proposed—a conceptual architecture integrating ontologies, knowledge graphs, semantic reasoning, and Large Language Models to automate OBE-aligned curriculum mapping.
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).
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