Abstract

The need for effective program evaluation in the agricultural industry is paramount to making efficient and well-informed decisions that meet the global demands for increasing agricultural productivity. Thus, the purpose of our study was to investigate the intersection of artificial intelligence (AI) and program evaluation, emphasizing its potential implications and applicability in agricultural contexts. Using integrative literature review methods, we reviewed existing literature to identify and analyze various AI tools and techniques that could be beneficial to program evaluators. Through our qualitative study, we identified eight emerging themes: Machine learning, Chat GPT, predictive analytics, data mining, generative AI, AI in evaluation training, cost-efficiency of AI, and trust in AI. Our findings indicate AI has increasing potential to serve as a tool for program evaluators across the program evaluation process. However, along with the need for more research to harness AI’s full potential while minimizing any risks, maintaining human oversight and ethical considerations is essential. We recommend continued education and training initiatives to equip evaluators with the skills necessary to use AI in the evaluation process.

Keywords: Agriculture, Artificial Intelligence, Education, Program Evaluation

How to Cite:

Benitez, J. A., Diaz-Manrique, M., Palmer, K., Fuller, E. R., Landaverde, R., Leggette, H. R., & Wingenbach, G. (2026). Agricultural program evaluation meets artificial intelligence: An integrative literature review brief. Journal of Applied Communications, 110(1), 1–10. https://doi.org/10.4148/jac.20854