Generative AI in Higher Education: Uncertain Students, Ambiguous Use Cases, and Mercenary Perspectives

Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading. Method: I surveyed 733 undergraduates. Results: Students reported mixed levels of experience with AI and tended toward nervousness over excitement. Most reported professors addressing AI but not integrating it. While 41% of students had used AI in ways explicitly banned, many more students (59%) reported ambiguous use cases. Students overestimated peer cheating, and this predicted their own cheating, as did general experience with and excitement about AI. Meanwhile, 11% of students reported false accusations, with first-generation students possibly at a higher rate. Pragmatic views about career and inequality may be affecting behaviors. Men consistently reported more involvement with AI than women. Conclusion: Future research should focus on the hybrid collaboration of humans and AI and how AI might be leveraged to support and scaffold genuine learning. Teaching Implications: AI will be relevant to many future careers, and students increasingly want it to be part of their education. Academic integrity will be a continuing challenge, and students need transparency.