A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education

2026-07-13

AI Summary

This study addresses the current lack of systematic comparison between institutional policies and computer science course practices regarding generative AI adoption in higher education. Through secondary analysis of institutional policy documents and computer science syllabi from U.S. research-intensive universities, it reveals a notable divergence: while institutional policies broadly encourage the use of generative AI, course-level implementations remain cautious and restrained. Building on this insight, the study proposes a faculty-centered framework for generative AI adoption in teaching, offering both empirical evidence and theoretical guidance to inform policy development and instructional practice in computing education.

Abstract

With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning are significant. At the same time, instructors have also been forced to address the use of GenAI as students have started to use it for a range of functions. Currently, comparative analysis of guidance provided by institutions and its uptake in instruction is lacking. In this paper we bridge this gap by comparing institutional and computing course level guidance to better understand this terrain. We utilize secondary analysis of institutional and course syllabi guidelines from higher education institutions in the U.S. classified as research-intensive. Our findings reveal that although institutional guidance is more pro-use, at the course-level the uptake is still guarded. We discuss the implications and propose an instructor-centered framework to guide future adoption of GenAI.

Problem

• generative AI
• higher education
• institutional policy
• computing education
• course syllabi

Innovation

• generative AI
• policy analysis
• computing education
• instructor-centered framework
• higher education