<![CDATA[Artificial Intelligence (AI) and predictive analytics are increasingly reshaping contemporary agricultural systems by enabling data-driven decision support, early pest and disease detection, and optimized crop management. As these technologies become embedded in agricultural practice, agricultural education faces growing pressure to prepare learners with analytical reasoning and decision-making competencies aligned with AI-enabled environments. This scoping review maps peer-reviewed literature published between 2020 and 2025 on AI-enabled predictive analytics, crop advisory systems, and pest and disease detection technologies, with a specific focus on their educational implications. Following PRISMA-ScR guidelines, 18 studies were systematically identified and synthesized through thematic analysis. The results indicate rapid growth in technically oriented AI applications for precision agriculture, contrasted with limited empirical research on curriculum integration and competency-based learning outcomes. While project-based learning, simulations, and decision support tools are frequently proposed as pedagogical strategies, explicit assessment of learners’ analytical and decision-making competencies remains scarce. This review highlights critical gaps between AI innovation and educational research, and underscores the need for interdisciplinary approaches, curriculum redesign, and competency frameworks that support responsible and effective AI use in agricultural education.]]>
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