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Preprint Jul 2026

Generative AI Availability, Grades, and Student Satisfaction at a Large University

It is found that there is no significant differential effect of GenAI availability on grades overall or among previously lower-performing students, and the findings temper concerns that GenAI inflates grades and reduces students's satisfaction.

J. Dumlao, Meng Wang, Zhonghan Xie et al. · 0 citations
Preprint Jul 2026

To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies

While generative AI tools are directly changing how undergraduate computer science is learned and taught, they are also reshaping the relationships between instructors and students. In contrast to existing tool-oriented research on how instructors view and adopt AI, this study investigates how instructors think about their roles and responsibilities to students through their course AI policies. Based on 13 semi-structured interviews with CS instructors in the US, we found that while instructors recognize that AI tools could harm student learning, AI policies primarily seek to AI-proof assessments without directly addressing student learning. Although policies such as switching to paper exams can preserve assessment integrity in the short term, instructors report extra burden of policing student AI use behaviors and worsening relationships with students. Based on the experiences of several interviewees, we make recommendations on AI policies that are more learning-oriented and could guide students toward healthier AI usage instead.

X. Gu, W. L. Santo, J. Dumlao et al. · 0 citations