Inteligencia artificial en la educación agrícola: un análisis de los modelos de aprendizaje personalizado

Agricultural education faces challenges in personalizing learning and adapting to the individual needs of students. In this context, artificial intelligence (AI) is positioned as a key tool to optimize educational models in the sector. This study aims to analyze the application of AI-based personalized learning models in agricultural education, identifying their benefits, limitations and implementation opportunities. For this purpose, a systematic literature review was conducted using indexed databases such as Scopus, Web of Science and ScienceDirect, covering studies published between 2019 and 2025. Inclusion criteria were applied that prioritized peer-reviewed research on AI in agricultural education, excluding papers with methodological shortcomings. Information was organized into key categories, including impact on teaching, technological barriers, and adoption strategies. The findings highlight that AI enhances personalization of learning through intelligent tutoring systems, predictive analytics, and simulation tools. However, its implementation faces challenges related to technological infrastructure, digital literacy, and resistance to change in traditional educational models. In addition, ethical concerns arise regarding data privacy and equity of access to these technologies. It is concluded that the integration of AI in agricultural education has the potential to transform teaching, optimize learning and strengthen the training of professionals in the sector. However, its success will depend on investment in infrastructure, equitable access policies and teacher training strategies.