Scoping Review of AI-Enabled Predictive Analytics and Decision Support in Agricultural Education

Current Trends, Tools, and Pedagogical Implications

Authors

DOI:

https://doi.org/10.56741/IISTR.jetia.001994


Keywords:

Agricultural Education, Artificial Intelligence, Decision Support Systems, Predictive Analytics, Scoping Review

Abstract

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.

Author Biographies

Daniela Laura, Universidad Mayor de San Simón

is an agroindustrial engineer, graduated from the Universidad Mayor de San Simón, Cochabamba, Bolivia. She holds postgraduate studies in Industrial Business Management and Technology completed in Spain, a specialization in Integrated Management Systems, and formal training in Human Rights, Indigenous Peoples, and International Cooperation from Universidad Carlos III of Madrid (Spain). She serves as a Research Professor at the Universidad Mayor de San Simón (UMSS) and as Academic Director of the Instituto de Estudios Latinoamericanos (INESLA). (email: da.laura@umss.edu.bo).

Nancy V. Quispe Cordova, National University of Juliaca

is a food industry engineer who graduated from the National University of Juliaca, Puno, Peru. Her academic and research interests focus on the valorization of grains and Andean agricultural products, particularly in developing sustainable food innovations and enhancing the added value of local resources. She is actively engaged in research related to food processing, quality, and regional agri-food development. (email: nv.quispec@unaj.edu.pe).

Franklin Ore Areche, National University of Huancavelica

is a faculty member at the Professional School of Agroindustrial Engineering, National University of Huancavelica, Peru. His academic focus lies in agroindustrial systems, food technology, and sustainable production processes in rural and mountainous regions. With a deep commitment to regional development, he actively contributes to research and academic programs promoting agroindustry innovation, emphasizing value-added agricultural production and environmental sustainability. (email: franklin.ore@unh.edu.pe).

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Published

2026-03-20

How to Cite

Laura, D., Cordova, N. V. Q., & Areche, F. O. (2026). Scoping Review of AI-Enabled Predictive Analytics and Decision Support in Agricultural Education: Current Trends, Tools, and Pedagogical Implications. Journal of Educational Technology Innovation and Applications, 2(01), 21–31. https://doi.org/10.56741/IISTR.jetia.001994

Issue

Vol. 2 No. 01 (2026): Journal of Educational Technology Innovation and Applications

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Articles

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Copyright (c) 2026 Daniela Laura, Nancy V. Quispe Cordova, Franklin Ore Areche

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