<![CDATA[The rapid growth of mobile technologies has reshaped how learning systems are designed, deployed, and evaluated, particularly in vocational education contexts. From a Mobile Software Engineering perspective, learning platforms must address constraints such as short interaction cycles, heterogeneous devices, scalability, and real-time analytics. This study focuses on the design and engineering of an AI-enabled mobile microlearning application that integrates short-form video, learning analytics, and LMS services to support vocational students’ soft-skills development. The proposed system is engineered as a mobile-first application with modular micro-content (60–180 seconds), rule-based personalization, and event-driven analytics to capture user interaction patterns. A Research and Development approach using the ADDIE framework is adopted, with emphasis on the software design, architecture, and prototyping stages. Validation involves expert review of system usability, content–software alignment, and limited pilot testing with end users. The results demonstrate that a mobile-engineered microlearning system can achieve high completion rates, acceptable latency under concurrent access, and effective analytics-driven feedback loops. The study contributes a practical mobile software engineering artefact and design insights for AI-enabled learning applications in vocational education.]]>
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