Generative AI in Higher Education: Analyzing Adoption Patterns and Perceptions in Agriculture and Natural Resources Courses

Bhawna Thapa, Aaron Russell and Omkar Joshi

Applied Economics Teaching Resources (AETR), 2025, vol. 7, issue 4

Abstract: This study investigates empirical data on how students and educators perceive the use of generative artificial intelligence (AI) in agriculture and natural resources (ANR) courses. By surveying participants at a land-grant university, the research explores how different educational backgrounds and sociodemographic factors influence attitudes toward AI adoption. The findings reveal that less than half of the respondents currently use generative AI, with significantly lower usage among first-year and rural students. Key drivers encouraging AI adoption include perceived academic benefits, ease of use, and familiarity with the technology. In contrast, barriers such as concerns about reliability, potential misuse, and information overload deter usage. Seniors and graduate students are more likely to embrace generative AI tools, whereas older and rural students show lower adoption rates. The Analytical Hierarchical Process underscores the necessity for tailored strategies to address specific concerns like inaccurate information and how to leverage AI's advantages, such as streamlining tasks for instructors and providing grammar assistance for students. Future course curricula and institutional policies should incorporate targeted training and additional support to meet specific educational needs, thereby enhancing learning outcomes and ensuring equitable access to the benefits of generative AI tools.

Keywords: Teaching/Communication/Extension/Profession (search for similar items in EconPapers)
Date: 2025
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