Linked dataset of institutional GenAI policies and CS course syllabi from U.S. R1 universities

Product or Programme · All products and programmes
Evidence · 4 claims (4 for) · 1 study (1 qualitative), q2 · 0 of 1 report an effect size · 4 claims rest on one study

Description

A research dataset created by Amrita Ganguly et al. that links publicly available GenAI policy documents from U.S. R1 universities with coded computer-science course syllabi to study alignment between institutional and course-level guidance.

Components

Claims

• Course syllabi name specific GenAI tools — ChatGPT in 90 of 98 — while institutional guidelines contain no code for specific tools [+W]
• Comprehensive institutional GenAI guidelines translate to course-level guidelines at higher rates (48.3–71.4%) than moderately comprehensive ones (30.8–31.2%) [+W]
• Institutional guidance cautions instructors about privacy (60% of institutions) while syllabi more often flag veracity of GenAI output and hindering learning [+W]
• Institutional GenAI guidance is more pro-use than computing course syllabus policies, where 92% of syllabi set explicit permissions and half prohibit use outright [+W]

Related Products and Programmes

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Key Sources

• Amrita Ganguly, Aditya Johri, Nora McDonald, Areej Ali, Umama Dewan, and Aayushi Hingle Collier. (2026). A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education.

Page metadata

Field	Value
type	product
id	linked-dataset-of-institutional-genai-policies-and-cs-course-syllabi-from-us-r1-universiti
title	Linked dataset of institutional GenAI policies and CS course syllabi from U.S. R1 universities
description	A research dataset created by Amrita Ganguly et al.
status	review
product_kind	dataset
generated	by process:settle-candidates on 2026-10-11
trust	unverified — no one has confirmed this page's content

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