To Police or to Guide: How Higher Education Computer Science Instructors Design and Implement Generative AI Policies
Computers and Society
Summary
The authors studied how computer science teachers in the US think about their roles when making rules about AI tools in their classes. They found that teachers mostly focus on preventing cheating by designing tests that AI can't easily solve, instead of helping students learn better with AI. This approach can make teachers' jobs harder and strain their relationships with students. The authors suggest making rules that help students use AI in smarter, more positive ways instead.
Authors
Xingjian, Gu, Wells Lucas Santo, James M. Zumel Dumlao, Barbara Ericson
Abstract
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.