AI-Driven Curriculum Analytics and Adaptive Learning Model for Next-Generation Technical and Vocational Education Systems
Keywords:
Adaptive curriculum design, Artificial intelligence in education, Curriculum analytics, Data-driven learning systems, Educational data mining, Industry 4.0 skills, Natural language processing, Reinforcement learning, Semantic similarity, Technical and Vocational Education and Training (TVET)Abstract
Technical and Vocational Education and Training (TVET) must evolve into a dynamic, data informed system to remain aligned with rapidly changing Industry 4.0 demands. This paper proposes a novel Curriculum Intelligence Model (CIM) that integrates natural language processing (NLP), semantic embedding, bibliometric analysis, and reinforcement learning to quantitatively assess and iteratively optimize TVET curricula. By mining corpora of research publications, job-market postings, and institutional syllabi, the model computes a Curriculum Relevance Index (CRI)—a weighted function of semantic similarity, Research–Discipline Integration (RDI), and industry collaboration activity (ICA). A Q-learning agent recommends curriculum modifications to maximize CRI over time. Results from pilot implementations show CRI improvements of 25% to 46% and semantic skill-overlaps increasing from ≈ 62% to 87%. A web dashboard prototype makes the analytics actionable for educators, enabling continuous curriculum evolution. The approach bridges educational theory, industry needs, and computational science, offering a replicable model for adaptive, future-ready TVET systems.
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