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Beyond the Textbook: Students’ Experiences Learning Agricultural Policy with an AI Tutor

Author

Listed:
  • Huber, Robert
  • Mihálka, Réka

Abstract

This study explores the integration of an AI-powered tutor into agricultural policy education at ETH Zürich to enhance the learning experience and provide insights into AI tools in higher education. Based on a large language model (ChatGPT-4.0), the AI tutor was designed to facilitate interactive learning about agricultural policy, specifically tailored to a textbook on Swiss agricultural policy. It provided functionalities such as concept clarification, summaries, knowledge tests, and open-ended discussions for undergraduate students. Over the course of the semester, 15 students used the tutor independently and for exam preparation. Analysis of student interactions revealed that 79 percent of the tutor’s use was for explaining and clarifying concepts, while 9 percent was for summaries and 4 percent for assessments. Students rated their overall satisfaction with the tutor as 3.8 out of 5 and perceived it as a supplementary learning tool. The results provide insights into the benefits, challenges, and ethical considerations of AI in education and highlight the potential for broader applications in other courses. The study contributes to the discourse on AI in higher education and guides the development and integration of AI-enhanced learning tools to improve student engagement and learning outcomes.

Suggested Citation

  • Huber, Robert & Mihálka, Réka, 2025. "Beyond the Textbook: Students’ Experiences Learning Agricultural Policy with an AI Tutor," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 7(5), November.
    • Handle: RePEc:ags:aaeatr:384749
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    File URL: https://ageconsearch.umn.edu/record/384749/files/AETR_2025_0263%20Full.pdf
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    References listed on IDEAS

    as
    1. Alexander Bick & Adam Blandin & David J. Deming, 2026. "The Rapid Adoption of Generative AI," Management Science, INFORMS, vol. 72(10), pages 8440-8456, October.
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