Use of an artificial intelligence system to learn organic fertilization with probit analysis
DOI:
https://doi.org/10.37711/desafios.2025.16.2.7Abstract
Objective: To evaluate the impact of an artificial intelligence (AI) system specialized in organic fertilization recommendations—based on local soil and crop data—on the learning outcomes of Agronomy students. Methods: A quasi-experimental design was applied to a sample of 200 higher education students majoring in Agronomy. A validated questionnaire was administered, and a probit econometric model was used to estimate the probability of academic success. Results: Findings indicate that the use of AI significantly increased the likelihood of effective learning (B = 0.896, p = 0.0023). Previous agricultural experience showed a marginally significant effect, while variables such as age, gender, and academic semester were not statistically significant. Conclusions: Contextualized AI is an effective pedagogical tool to enhance the understanding of organic fertilization among Agronomy students, fostering a more sustainable, resilient, and equitable agricultural education.
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Copyright (c) 2025 Marco Vizuete Montero
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