<![CDATA[This study proposes an artificial intelligence (AI)-driven framework that integrates learning analytics with operational optimization to address the fragmentation between educational data and operational decision-making in technology and vocational education. Although vocational institutions increasingly adopt digital technologies, learning-related data are often analyzed separately from operational processes such as scheduling, task allocation, resource utilization, and process efficiency, despite their interdependence in teaching factory and practical learning environments. Drawing on perspectives from industrial engineering and informatics, the proposed framework integrates machine learning for analyzing learner performance and competency development with optimization techniques for supporting operational decision-making. The framework was designed to be adaptable across different vocational education contexts and was empirically evaluated in a technology-oriented vocational setting to assess its feasibility. The evaluation demonstrates the feasibility of integrating learner-related and operational information within a unified decision-support environment, enabling competency and performance information to inform decisions concerning practical tasks, scheduling, and resource allocation. Rather than positioning AI solely as a predictive or instructional tool, the framework conceptualizes it as a mechanism for connecting pedagogical and operational dimensions of vocational learning. This study contributes an interdisciplinary and system-oriented perspective on AI adoption in vocational education, providing a foundation for more integrated, context-sensitive, and data-informed decision-making in teaching factory and practical learning environments.]]>
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