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

2026-07-17

AI Summary

This study addresses the tension faced by computer science faculty in higher education between upholding academic integrity and supporting student learning amid the widespread adoption of generative AI. Current institutional policies often emphasize surveillance and deterrence, which not only increase instructor workload but also erode trust in student–faculty relationships. Drawing on semi-structured interviews with thirteen computer science instructors at U.S. universities, the research employs qualitative thematic analysis to uncover how these educators conceptualize their roles and navigate decision-making when formulating AI-use policies. Moving beyond a tool-centric paradigm, the study proposes a learner-centered, guided-policy framework that shifts the emphasis from control to empowerment, offering both theoretical grounding and practical pathways for developing pedagogical norms that foster students’ responsible and constructive engagement with AI.

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.

Problem

• generative AI
• AI policy
• computer science education
• instructor role
• student learning

Innovation

• generative AI
• AI policy
• computer science education
• instructor role
• learning-oriented design