A Conceptual Teaching Framework For AI-Ready IoT System Design in TVET Education

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is reshaping industry expectations of Technical and Vocational Education and Training (TVET) graduates, who are increasingly required to work with connected, data-driven systems. However, many IoT courses remain component-based, emphasizing sensors, microcontrollers, communication, and dashboards in isolation. While this approach enables students to build functional prototypes, it often limits their understanding of overall system architecture and data readiness for future AI integration. This paper argues that the key challenge lies not in the absence of AI instruction, but in the way foundational IoT concepts are taught. It proposes a shift toward system-oriented and data-driven IoT education, where AI readiness emerges as a natural outcome of sound system design rather than advanced algorithm training. To support this shift, the paper introduces a conceptual teaching framework consisting of four layers: sensing, connectivity, data readiness, and application intelligence to guide the organization of IoT projects and laboratory activities. The proposed framework offers a practical approach for modernizing TVET IoT courses by promoting structured data generation and system-level thinking, while also providing a foundation for future empirical studies on AIoT learning outcomes.