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Using Generative Artificial Intelligence to Aid Classroom Retention

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  • Pruitt, J. Ross
  • Delmond, Anthony R.
  • Mehlhorn, Sandy
  • Watson, Diana L.

Abstract

The use of generative artificial intelligence (AI), which includes tools such as ChatGPT, Bing, and Bard, allows users to find information for specific questions with just a few keystrokes. While this technology is not a replacement for traditional research methods, it can help undergraduate agriculture students be efficient in their time management skills as they move through the various stages associated with writing papers. The question remains whether students increase their retention of knowledge from use of generative AI in conjunction with traditional course lectures. Participants in this research were provided with a video describing generative AI and then completed a course assignment using this technology. Using a pre- and post-evaluation, agriculture students self-assessed how use of generative AI aided retention of knowledge. Questions on the evaluation addressed whether students view generative AI as ethical to use for course assignments and in a professional business environment, if it will aid their future career plans, and if they are more likely to use generative AI due to the assignment. Use of generative AI in conjunction with a course assignment can aid in improved understanding of the benefits and drawbacks associated with this technology. Our analysis provides information on students’ prior use of this technology and how it can benefit their retention of knowledge. Results indicate the extent to which students believe use of AI is ethical in business or professional settings, and previously earned dual enrollment credit indicates their retention of knowledge and change in beliefs toward its usefulness in future careers. Students were largely neutral on AI, aiding retention of knowledge more than a traditional lecture or their normal study methods.

Suggested Citation

  • Pruitt, J. Ross & Delmond, Anthony R. & Mehlhorn, Sandy & Watson, Diana L., 2025. "Using Generative Artificial Intelligence to Aid Classroom Retention," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 7(3), April.
  • Handle: RePEc:ags:aaeatr:377651
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    References listed on IDEAS

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    1. Black, Michael, 2020. "Insights from Asynchronous Lecture Viewing Behavior," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 2(5), December.
    2. Lupton, Danielle L., 2019. "The External Validity of College Student Subject Pools in Experimental Research: A Cross-Sample Comparison of Treatment Effect Heterogeneity," Political Analysis, Cambridge University Press, vol. 27(1), pages 90-97, January.
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