The rapid evolution of digital economies has intensified the demand for advanced vocational competencies, particularly in digital content production at competency Level 3 in Thailand. This study proposes a Smart Learning Media Architecture (SLMA) designed to optimize occupational skill development through adaptive, data-driven, and competency-aligned learning systems. The architecture integrates intelligent learning analytics, experiential pedagogy, and competency-based education frameworks to enhance learner performance in vocational education and training (VET) contexts. Drawing on established theoretical foundations such as experiential learning theory (Kolb, 1984), competency-based assessment (Boyatizis, 1982), and problem-based learning models (Barrows & Tamblyn, 1980), the study constructs a multi-layered instructional system incorporating adaptive content delivery, learner modeling, and performance evaluation modules.
A design science research methodology is employed to conceptualize, develop, and evaluate the SLMA framework. Data-driven optimization strategies are embedded using educational data mining principles inspired by Berry & Gordon (2004), enabling the system to analyze learner behavior and improve instructional efficiency. The evaluation framework assesses usability, competency gain, and learning effectiveness in vocational digital content training environments. Findings suggest that the proposed architecture significantly enhances learner engagement, skill acquisition, and adaptive learning pathways. The study contributes to the integration of intelligent systems in vocational education, offering a scalable model for digital skills development in emerging economies.

Smart Learning Media Architecture, Vocational Education, Digital Content Competency, Learning Analytics

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