<![CDATA[The integration of Generative Artificial Intelligence (GenAI) in vocational education is expanding rapidly; however, evidence-based references for selecting prompt engineering frameworks suited to the vocational learning context remain scarce. Without structured guidance, the potential of GenAI to enhance learning outcomes and the quality of vocational curricula cannot be fully realized. This systematic review addresses this gap by critically examining the role of prompt engineering in AI utilization within vocational education and evaluating the effectiveness of prompt engineering frameworks for curriculum integration, distinguishing between their applications in general and vocational subjects. Following PRISMA guidelines, a systematic literature review was conducted by searching reputable academic databases relevant to education, technology, and artificial intelligence. The review focused on recent publications reflecting the evolution of GenAI in educational contexts. From the initial pool of articles, only those meeting the inclusion criteria underwent further analysis using thematic content analysis. The findings reveal that prompt engineering plays several crucial roles: strengthening teaching practices, supporting student learning through scaffolding, and improving the reliability of AI outputs. The CRISPE framework was deemed most effective for general subjects characterized by interpretative and exploratory approaches. In contrast, CLEAR was found more suitable for vocational subjects requiring structured, step-by-step responses. These findings offer practical implications for vocational educators, curriculum developers, and policymakers on integrating AI literacy into vocational education curricula.]]>
