Generative AI in Undergraduate Education: An Early View of Developments, Prospects, and Challenges of the AI Revolution
Terence Day, Matteo Gonzalez, Junghwan Kim, Paul N. McDaniel, Kyle Redican, Tingting Zhu · The Professional Geographer · 2025
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. 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.