Generative AI in Undergraduate Geography Education
“Across all disciplines, generative artificial intelligence (GenAI) threatens student academic integrity in traditional assessments. Its detection is unreliable. From talking with students, however, we know they are finding GenAI to be helpful in their studies,” begins the abstract to our recent article, “Generative AI in Undergraduate Education: An Early View of Developments, Prospects, and Challenges of the AI Revolution,” published in the Professional Geographer, one of the peer-reviewed journals of the American Association of Geographers (AAG). Our article’s authorship team consists of Terence Day (Okanagan College), Matteo Gonzalez (University of Richmond), Junghwan Kim (Virginia Tech), Paul N. McDaniel (Kennesaw State University), Kyle Redican (University of Richmond), and Tingting Zhu (University of Toronto).
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As the abstract description continues, “Through experiments and experience at five universities and colleges in the United States and Canada, this article demonstrates that GenAI can be strategically, thoughtfully, and critically deployed to improve postsecondary geography teaching and learning. Our experiments show that faculty can potentially create more efficient workflows by using GenAI to create assignments, multiple-choice questions, rubrics, and generalized feedback on assignments. We stress that GenAI output needs to be checked, but the time saved can be used to foster deeper student understanding and engagement with geographic concepts, and to assist students who are struggling. At the same time assessments need to be reimagined to incorporate the new realities of GenAI and we provide an example ‘spot the mistake(s)’ type of question. Students and faculty need to be educated on the new technologies, not just for educational use, but as students move into careers.”
Continue reading online in Professional Geographer here.
